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Identifying Key Issues in Integration of Autonomous Ships in Container Ports: A Machine-Learning-Based Systematic Literature Review

Author

Listed:
  • Enna Hirata

    (Graduate School of Maritime Sciences, Center for Mathematical and Data Sciences, Kobe University, Higashinada-ku, Kobe 658-0022, Japan)

  • Annette Skovsted Hansen

    (School of Culture and Society, Aarhus University, 8000 Aarhus, Denmark)

Abstract

Background: Autonomous ships have the potential to increase operational efficiency and reduce carbon footprints through technology and innovation. However, there is no comprehensive literature review of all the different types of papers related to autonomous ships, especially with regard to their integration with ports. This paper takes a systematic review approach to extract and summarize the main topics related to autonomous ships in the fields of container shipping and port management. Methods: A machine learning method is used to extract the main topics from more than 2000 journal publications indexed in WoS and Scopus. Results: The research findings highlight key issues related to technology, cybersecurity, data governance, regulations, and legal frameworks, providing a different perspective compared to human manual reviews of papers. Conclusions: Our search results confirm several recommendations. First, from a technological perspective, it is advised to increase support for the research and development of autonomous underwater vehicles and unmanned aerial vehicles, establish safety standards, mandate testing of wave model evaluation systems, and promote international standardization. Second, from a cyber–physical systems perspective, efforts should be made to strengthen logistics and supply chains for autonomous ships, establish data governance protocols, enforce strict control over IoT device data, and strengthen cybersecurity measures. Third, from an environmental perspective, measures should be implemented to address the environmental impact of autonomous ships. This can be achieved by promoting international agreements from a global societal standpoint and clarifying the legal framework regarding liability in the event of accidents.

Suggested Citation

  • Enna Hirata & Annette Skovsted Hansen, 2024. "Identifying Key Issues in Integration of Autonomous Ships in Container Ports: A Machine-Learning-Based Systematic Literature Review," Logistics, MDPI, vol. 8(1), pages 1-15, February.
    • Handle: RePEc:gam:jlogis:v:8:y:2024:i:1:p:23-:d:1342795
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    References listed on IDEAS

    as
    1. Ziaul Haque Munim & Hercules Haralambides, 2022. "Advances in maritime autonomous surface ships (MASS) in merchant shipping," Post-Print hal-04046263, HAL.
    2. Ziaul Haque Munim & Hercules Haralambides, 2022. "Advances in maritime autonomous surface ships (MASS) in merchant shipping," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 24(2), pages 181-188, June.
    3. Li, Xue & Oh, Poong & Zhou, Yusheng & Yuen, Kum Fai, 2023. "Operational risk identification of maritime surface autonomous ship: A network analysis approach," Transport Policy, Elsevier, vol. 130(C), pages 1-14.
    4. Ismail Kurt & Murat Aymelek, 2022. "Operational and economic advantages of autonomous ships and their perceived impacts on port operations," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 24(2), pages 302-326, June.
    5. Utne, Ingrid Bouwer & Rokseth, Børge & Sørensen, Asgeir J. & Vinnem, Jan Erik, 2020. "Towards supervisory risk control of autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    6. Carmen Kooij & Robert Hekkenberg, 2022. "Identification of a task-based implementation path for unmanned autonomous ships," Maritime Policy & Management, Taylor & Francis Journals, vol. 49(7), pages 954-970, October.
    7. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    8. Ziaul Haque Munim & Hercules Haralambides, 2022. "Advances in maritime autonomous surface ships (MASS) in merchant shipping," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-04046263, HAL.
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