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How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications

Citations

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Cited by:

  1. Hongze Liu & Irena Jurdana & Nikola Lopac & Nobukazu Wakabayashi, 2022. "BlueNavi: A Microservices Architecture-Styled Platform Providing Maritime Information," Sustainability, MDPI, vol. 14(4), pages 1-19, February.
  2. Harilaos N. Psaraftis & Christos A. Kontovas, 2020. "Decarbonization of Maritime Transport: Is There Light at the End of the Tunnel?," Sustainability, MDPI, vol. 13(1), pages 1-16, December.
  3. Zhang, Mingyang & Kujala, Pentti & Hirdaris, Spyros, 2022. "A machine learning method for the evaluation of ship grounding risk in real operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
  4. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
  5. Bai, Xiwen & Hou, Yao & Yang, Dong, 2021. "Choose clean energy or green technology? Empirical evidence from global ships," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
  6. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Li, Huanhuan & Ekere, Nduka & Yang, Zaili, 2023. "Multi-scale collision risk estimation for maritime traffic in complex port waters," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
  7. Gast, Johannes & Binsfeld, Tom & Marsili, Francesca & Jahn, Carlos, 2021. "Analysis of the Suez Canal blockage with queueing theory," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 943-959, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
  8. Sugrue, Dennis & Adriaens, Peter, 2021. "A data fusion approach to predict shipping efficiency for bulk carriers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
  9. Peng, Wenhao & Bai, Xiwen, 2022. "Prospects for improving shipping companies’ profit margins by quantifying operational strategies and market focus approach through AIS data," Transport Policy, Elsevier, vol. 128(C), pages 138-152.
  10. Bai, Xiwen & Cheng, Liangqi & Yang, Dong & Cai, Ouchen, 2022. "Does the traffic volume of a port determine connectivity? Revisiting port connectivity measures with high-frequency satellite data," Journal of Transport Geography, Elsevier, vol. 102(C).
  11. Lorenz Kolley & Nicolas Rückert & Marvin Kastner & Carlos Jahn & Kathrin Fischer, 2023. "Robust berth scheduling using machine learning for vessel arrival time prediction," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 29-69, March.
  12. Kerbiriou, Ronan & Serry, Arnaud, 2023. "Estimation and analysis of container handling rates in European ports," Journal of Transport Geography, Elsevier, vol. 108(C).
  13. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
  14. Chung, Sai-Ho, 2021. "Applications of smart technologies in logistics and transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
  15. Wang, Yukuan & Liu, Jingxian & Liu, Ryan Wen & Wu, Weihuang & Liu, Yang, 2023. "Interval prediction of vessel trajectory based on lower and upper bound estimation and attention-modified LSTM with bayesian optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
  16. Li, Yiliang & Bai, Xiwen & Wang, Qi & Ma, Zhongjun, 2022. "A big data approach to cargo type prediction and its implications for oil trade estimation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
  17. Feng, Mingxiang & Shaw, Shih-Lung & Peng, Guojun & Fang, Zhixiang, 2020. "Time efficiency assessment of ship movements in maritime ports: A case study of two ports based on AIS data," Journal of Transport Geography, Elsevier, vol. 86(C).
  18. Zhang, Jinfen & Liu, Jiongjiong & Hirdaris, Spyros & Zhang, Mingyang & Tian, Wuliu, 2023. "An interpretable knowledge-based decision support method for ship collision avoidance using AIS data," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  19. Yang, Dong & Liao, Shiguan & Venus Lun, Y.H & Bai, Xiwen, 2023. "Towards sustainable port management: Data-driven global container ports turnover rate assessment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
  20. Konstantinos Poulis & Gregorios C. Galanakis & Gregory T. Triantafillou & Efthimios Poulis, 2020. "Value migration: digitalization of shipping as a mechanism of industry dethronement," Journal of Shipping and Trade, Springer, vol. 5(1), pages 1-18, December.
  21. Kei Kanamoto & Liwen Murong & Minato Nakashima & Ryuichi Shibasaki, 2021. "Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carriers," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 211-236, June.
  22. Li, Lu & Wan, Yulai & Yang, Dong, 2024. "Do shipping alliances affect freight rates? Evidence from global satellite ship data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
  23. Ahmadhon Akbarkhonovich Kamolov & Suhyun Park, 2021. "Prediction of Depth of Seawater Using Fuzzy C-Means Clustering Algorithm of Crowdsourced SONAR Data," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
  24. Mazurek, J. & Lu, L. & Krata, P. & Montewka, J. & Krata, H. & Kujala, P., 2022. "An updated method identifying collision-prone locations for ships. A case study for oil tankers navigating in the Gulf of Finland," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
  25. Yang, Dong & Wu, Lingxiao & Wang, Shuaian, 2021. "Can we trust the AIS destination port information for bulk ships?–Implications for shipping policy and practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
  26. Bai, Xiwen & Cheng, Liangqi & Iris, Çağatay, 2022. "Data-driven financial and operational risk management: Empirical evidence from the global tramp shipping industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
  27. Gil, Mateusz & Kozioł, Paweł & Wróbel, Krzysztof & Montewka, Jakub, 2022. "Know your safety indicator – A determination of merchant vessels Bow Crossing Range based on big data analytics," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
  28. 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).
  29. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Yang, Zaili, 2023. "Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  30. Bai, Xiwen & Xu, Ming & Han, Tingting & Yang, Dong, 2022. "Quantifying the impact of pandemic lockdown policies on global port calls," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 224-241.
  31. Zheng, Shiyuan & Jiang, Changmin, 2024. "Consortium blockchain in Shipping: Impacts on industry and social welfare," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
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