IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v8y2024i1p23-d1342795.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/8/1/23/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2305-6290/8/1/23/
    Download Restriction: no
    ---><---

    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. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hercules Haralambides, 2023. "The state-of-play in maritime economics and logistics research (2017–2023)," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(3), pages 429-451, September.
    2. Michael F. Gorman & John-Paul Clarke & René Koster & Michael Hewitt & Debjit Roy & Mei Zhang, 2023. "Emerging practices and research issues for big data analytics in freight transportation," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 28-60, March.
    3. Irina Wedel & Michael Palk & Stefan Voß, 2022. "A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter," Information Systems Frontiers, Springer, vol. 24(5), pages 1635-1646, October.
    4. Mohammed Salem Binwahlan, 2023. "Polynomial Networks Model for Arabic Text Summarization," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(2), pages 74-84, February.
    5. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    6. Chao Wei & Senlin Luo & Xincheng Ma & Hao Ren & Ji Zhang & Limin Pan, 2016. "Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    7. Maksym Polyakov & Morteza Chalak & Md. Sayed Iftekhar & Ram Pandit & Sorada Tapsuwan & Fan Zhang & Chunbo Ma, 2018. "Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 217-239, September.
    8. Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    9. Klaus Gugler & Florian Szücs & Ulrich Wohak, 2023. "Start-up Acquisitions, Venture Capital and Innovation: A Comparative Study of Google, Apple, Facebook, Amazon and Microsoft," Department of Economics Working Papers wuwp340, Vienna University of Economics and Business, Department of Economics.
    10. Juan Shi & Kin Keung Lai & Ping Hu & Gang Chen, 2018. "Factors dominating individual information disseminating behavior on social networking sites," Information Technology and Management, Springer, vol. 19(2), pages 121-139, June.
    11. Ganesh Dash & Chetan Sharma & Shamneesh Sharma, 2023. "Sustainable Marketing and the Role of Social Media: An Experimental Study Using Natural Language Processing (NLP)," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    12. Paola Cerchiello & Giancarlo Nicola, 2018. "Assessing News Contagion in Finance," Econometrics, MDPI, vol. 6(1), pages 1-19, February.
    13. Shr-Wei Kao & Pin Luarn, 2020. "Topic Modeling Analysis of Social Enterprises: Twitter Evidence," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    14. Gissler, Stefan & Oldfather, Jeremy & Ruffino, Doriana, 2016. "Lending on hold: Regulatory uncertainty and bank lending standards," Journal of Monetary Economics, Elsevier, vol. 81(C), pages 89-101.
    15. Wittek, Peter, 2013. "Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional datasets," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 193-201.
    16. Alina Evstigneeva & Mark Sidorovskiy, 2021. "Assessment of Clarity of Bank of Russia Monetary Policy Communication by Neural Network Approach," Russian Journal of Money and Finance, Bank of Russia, vol. 80(3), pages 3-33, September.
    17. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    18. Hei-Chia Wang & Tzu-Ting Hsu & Yunita Sari, 2019. "Personal research idea recommendation using research trends and a hierarchical topic model," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1385-1406, December.
    19. Hiroaki Sugino & Tatsuya Sekiguchi & Yuuki Terada & Naoki Hayashi, 2023. "“Future Compass”, a Tool That Allows Us to See the Right Horizon—Integration of Topic Modeling and Multiple-Factor Analysis," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    20. David A. Broniatowski, 2018. "Building the tower without climbing it: Progress in engineering systems," Systems Engineering, John Wiley & Sons, vol. 21(3), pages 259-281, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlogis:v:8:y:2024:i:1:p:23-:d:1342795. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.