IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v149y2021ics1366554521001009.html
   My bibliography  Save this article

A data fusion approach to predict shipping efficiency for bulk carriers

Author

Listed:
  • Sugrue, Dennis
  • Adriaens, Peter

Abstract

Maritime waterways are critical transportation systems that connect economies and manufacturing centers. Growing demand for freight movement, along with industry commitment to minimize its environmental impact, has increased emphasis on port and vessel efficiency. Yet, few objective performance measures exist to inform decision making for system improvements. There is an existing gap in quantifiable and objective metrics for maritime transport systems which motivated this work to investigate waterway performance efficiencies through big data analytics. Availability of big data affords practitioners and researchers the opportunity to develop new performance-based metrics to improve maritime logistics. This study focused on short sea shipping logistics of iron ore in the Great Lakes and makes three fundamental contributions. Principally, we propose a maritime transport efficiency (MTE) metric attained through fusion of data from the Automatic Identification System (AIS) and navigation lock data that integrates travel time and vessel payload. We present a linear model to predict vessel capacity based on water surface elevation which will enable practitioners to better adapt to seasonal changes and dredging needs specific to the Great Lakes. Additionally, we present travel time statistics for bulk carriers on the waterway observed through historical AIS data which extends the body of knowledge from earlier works and establishes a reference for system performance. Techniques presented here are effective in capturing travel time statistics in a non-linear interconnected system. This data-driven approach offers new insights for logistics planning and optimization with direct applications to short sea shipping and inland waterways systems. These insights to port and fleet performance allow for querying and simulation of cost impact from investment strategies aimed to improve efficiency or maximize value for operational expenses.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:transe:v:149:y:2021:i:c:s1366554521001009
    DOI: 10.1016/j.tre.2021.102326
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554521001009
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2021.102326?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dong Yang & Lingxiao Wu & Shuaian Wang & Haiying Jia & Kevin X. Li, 2019. "How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications," Transport Reviews, Taylor & Francis Journals, vol. 39(6), pages 755-773, November.
    2. Daniel Seong-Hyeok Moon & Jong Kyun Woo, 2014. "The impact of port operations on efficient ship operation from both economic and environmental perspectives," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(5), pages 444-461, September.
    3. Johnson, Hannes & Styhre, Linda, 2015. "Increased energy efficiency in short sea shipping through decreased time in port," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 167-178.
    4. Hee-Seok Bang & Hyo-Won Kang & Jeffrey Martin & Su-Han Woo, 2012. "The impact of operational and strategic management on liner shipping efficiency: a two-stage DEA approach," Maritime Policy & Management, Taylor & Francis Journals, vol. 39(7), pages 653-672, December.
    5. Harilaos N. Psaraftis, 2019. "Ship routing and scheduling: the cart before the horse conjecture," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(1), pages 111-124, March.
    6. 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.
    7. Mansouri, S. Afshin & Lee, Habin & Aluko, Oluwakayode, 2015. "Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 78(C), pages 3-18.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yap, Wei Yim & Hsieh, Cheng-Hsien & Lee, Paul Tae-Woo, 2023. "Shipping connectivity data analytics: Implications for maritime policy," Transport Policy, Elsevier, vol. 132(C), pages 112-127.
    2. Marco Gonçalves & Cátia Salgado & Amaro de Sousa & Leonor Teixeira, 2025. "Data Storytelling and Decision-Making in Seaport Operations: A New Approach Based on Business Intelligence," Sustainability, MDPI, vol. 17(1), pages 1-26, January.
    3. 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.
    4. 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).
    5. 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).

    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. 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).
    2. Wang, Tingsong & Cheng, Peiyue & Zhen, Lu, 2023. "Green development of the maritime industry: Overview, perspectives, and future research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    3. 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).
    4. 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).
    5. 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).
    6. 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.
    7. von Knorring, Hannes, 2019. "Energy audits in shipping companies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 125(C), pages 35-55.
    8. 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).
    9. Tran, Nguyen Khoi & Haasis, Hans-Dietrich, 2015. "An empirical study of fleet expansion and growth of ship size in container liner shipping," International Journal of Production Economics, Elsevier, vol. 159(C), pages 241-253.
    10. 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).
    11. Di Vaio, Assunta & Varriale, Luisa & Alvino, Federico, 2018. "Key performance indicators for developing environmentally sustainable and energy efficient ports: Evidence from Italy," Energy Policy, Elsevier, vol. 122(C), pages 229-240.
    12. Ali Najmi & Taha H. Rashidi & Alireza Abbasi & S. Travis Waller, 2017. "Reviewing the transport domain: an evolutionary bibliometrics and network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 843-865, February.
    13. Gunasekaran, Angappa & Subramanian, Nachiappan & Papadopoulos, Thanos, 2017. "Information technology for competitive advantage within logistics and supply chains: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 99(C), pages 14-33.
    14. 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).
    15. 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.
    16. Suneet Singh & Ashish Dwivedi & Saurabh Pratap, 2023. "Sustainable Maritime Freight Transportation: Current Status and Future Directions," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
    17. Michalis P. Michaelides & Herodotos Herodotou & Mikael Lind & Richard T. Watson, 2019. "Port-2-Port Communication Enhancing Short Sea Shipping Performance: The Case Study of Cyprus and the Eastern Mediterranean," Sustainability, MDPI, vol. 11(7), pages 1-22, March.
    18. Woo, Jong-Kyun & Moon, Daniel S.H. & Lam, Jasmine Siu Lee, 2018. "The impact of environmental policy on ports and the associated economic opportunities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 234-242.
    19. 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).
    20. Dai, Lei & Hu, Hao & Wang, Zhaojing, 2020. "Is Shore Side Electricity greener? An environmental analysis and policy implications," Energy Policy, Elsevier, vol. 137(C).

    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:eee:transe:v:149:y:2021:i:c:s1366554521001009. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.