IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i24p16711-d1297417.html
   My bibliography  Save this article

Extraction of Bunkering Services from Automatic Identification System Data and Their International Comparisons

Author

Listed:
  • Eisuke Watanabe

    (Graduate School of Engineering, University of Tokyo, Tokyo 113-8656, Japan)

  • Ryuichi Shibasaki

    (Graduate School of Engineering, University of Tokyo, Tokyo 113-8656, Japan)

Abstract

Despite the increased demand for alternative fuel bunkering and the importance of its base formation, quantitative information or statistics on bunkering are very limited due to data availability. This study aims to develop a quantitative method to extract bunkering operations and analyze and compare the actual bunkering operations, such as bunkering service times and starting times in the port area, by extracting anchored vessels using a clustering method and matching them with bunker barges spatio-temporally. The algorithm also reflects the characteristic behavior of bunker barges, including calling at refineries and bunker barge bases. This study then focuses on bunkering in three port areas and compares their characteristics from various perspectives. The study’s key findings reveal variations in vessel types and service times across three port areas, particularly in Tokyo Bay, where shorter service times are observed for containerships and dry bulk carriers due to high fuel prices. Additionally, it highlights differences in bunkering start times, with Tokyo Bay for daytime operations and Singapore Port for a more balanced distribution throughout the day. Furthermore, bunkering locations differ, with Tokyo Bay and Busan Port having most operations at container terminals, while offshore bunkering is prevalent in Singapore Port.

Suggested Citation

  • Eisuke Watanabe & Ryuichi Shibasaki, 2023. "Extraction of Bunkering Services from Automatic Identification System Data and Their International Comparisons," Sustainability, MDPI, vol. 15(24), pages 1-19, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16711-:d:1297417
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/24/16711/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/24/16711/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Murray, Brian & Perera, Lokukaluge Prasad, 2021. "An AIS-based deep learning framework for regional ship behavior prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Xuyang Han & Costas Armenakis & Mojgan Jadidi, 2021. "Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
    3. Gi-Young Chae & Seung-Hyun An & Chul-Yong Lee, 2021. "Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    4. Zhang, Liye & Meng, Qiang & Fang Fwa, Tien, 2019. "Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 287-304.
    5. Ghosh, Sugoutam & Lee, Loo Hay & Ng, Szu Hui, 2015. "Bunkering decisions for a shipping liner in an uncertain environment with service contract," European Journal of Operational Research, Elsevier, vol. 244(3), pages 792-802.
    6. Meng, Qiang & Wang, Shuaian & Lee, Chung-Yee, 2015. "A tailored branch-and-price approach for a joint tramp ship routing and bunkering problem," Transportation Research Part B: Methodological, Elsevier, vol. 72(C), pages 1-19.
    7. Liu, Baoli & Li, Zhi-Chun & Wang, Yadong, 2023. "A branch-and-price heuristic algorithm for the bunkering operation problem of a liquefied natural gas bunkering station in the inland waterways," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 145-170.
    8. Fuentes, Gabriel, 2021. "Generating bunkering statistics from AIS data: A machine learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    9. De, Arijit & Choudhary, Alok & Turkay, Metin & Tiwari, Manoj K., 2021. "Bunkering policies for a fuel bunker management problem for liner shipping networks," European Journal of Operational Research, Elsevier, vol. 289(3), pages 927-939.
    10. Wang, Ying & Yeo, Gi-Tae & Ng, Adolf K.Y., 2014. "Choosing optimal bunkering ports for liner shipping companies: A hybrid Fuzzy-Delphi–TOPSIS approach," Transport Policy, Elsevier, vol. 35(C), pages 358-365.
    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. Zhen, Lu & Wang, Shuaian & Zhuge, Dan, 2017. "Dynamic programming for optimal ship refueling decision," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 63-74.
    2. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2022. "Maritime traffic probabilistic prediction based on ship motion pattern extraction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Raimonds Aronietis & Christa Sys & Edwin van Hassel & Thierry Vanelslander, 2017. "Investigating the bunkering choice determinants: the case of the port of Antwerp," Journal of Shipping and Trade, Springer, vol. 2(1), pages 1-13, December.
    4. De, Arijit & Choudhary, Alok & Turkay, Metin & Tiwari, Manoj K., 2021. "Bunkering policies for a fuel bunker management problem for liner shipping networks," European Journal of Operational Research, Elsevier, vol. 289(3), pages 927-939.
    5. 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).
    6. Ksciuk, Jana & Kuhlemann, Stefan & Tierney, Kevin & Koberstein, Achim, 2023. "Uncertainty in maritime ship routing and scheduling: A Literature review," European Journal of Operational Research, Elsevier, vol. 308(2), pages 499-524.
    7. Yanhui Chen & Jinrong Lu & Mengmeng Ma, 2022. "How Does Oil Future Price Imply Bunker Price—Cointegration and Prediction Analysis," Energies, MDPI, vol. 15(10), pages 1-17, May.
    8. Li, Huanhuan & Jiao, Hang & Yang, Zaili, 2023. "AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    9. Wang, Zehao & Zeng, Qingcheng & Li, Xingchun & Qu, Chenrui, 2024. "A branch-and-price heuristic algorithm for the ART and external truck scheduling problem in an automated container terminal with a parallel layout," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    10. Magirou, Evangelos F. & Psaraftis, Harilaos N. & Bouritas, Theodore, 2015. "The economic speed of an oceangoing vessel in a dynamic setting," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 48-67.
    11. Wu, Lingxiao & Pan, Kai & Wang, Shuaian & Yang, Dong, 2018. "Bulk ship scheduling in industrial shipping with stochastic backhaul canvassing demand," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 117-136.
    12. Jiang, Meizhi & Lu, Jing, 2020. "The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 139(C).
    13. Agostino Bruzzone & Anna Sciomachen, 2023. "Simulating Operating Performance of Alternative Configurations of LNG Bunkering Stations," Sustainability, MDPI, vol. 15(13), pages 1-18, June.
    14. Wang, Shuaian & Qu, Xiaobo & Yang, Ying, 2015. "Estimation of the perceived value of transit time for containerized cargoes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 298-308.
    15. Wu, Weitiao & Liu, Ronghui & Jin, Wenzhou, 2016. "Designing robust schedule coordination scheme for transit networks with safety control margins," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 495-519.
    16. You, Qidong & Guo, Jianbin & Zeng, Shengkui & Che, Haiyang, 2024. "A dynamic Bayesian network based reliability assessment method for short-term multi-round situation awareness considering round dependencies," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    17. Yang, Zhisen & Wan, Chengpeng & Yu, Qing & Yin, Jingbo & Yang, Zaili, 2023. "A machine learning-based Bayesian model for predicting the duration of ship detention in PSC inspection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    18. 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).
    19. Wang, Yulan & Wallace, Stein W. & Shen, Bin & Choi, Tsan-Ming, 2015. "Service supply chain management: A review of operational models," European Journal of Operational Research, Elsevier, vol. 247(3), pages 685-698.
    20. Efrah Wozir Abdulahi & Luo Fan, 2021. "Exploring and Validating Container Operational Risk Scale in Container Shipping: The Case of Ethiopian Shipping and Logistics Service Enterprise," Sustainability, MDPI, vol. 13(16), pages 1-21, August.

    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:jsusta:v:15:y:2023:i:24:p:16711-:d:1297417. 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.