IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0289607.html
   My bibliography  Save this article

Risk assessment for yachting tourism in China using dynamic Bayesian networks

Author

Listed:
  • Yunhao Yao
  • Xiaoxing Zhou
  • Merle Parmak

Abstract

Scientific evaluation of yachting tourism safety risks (YTSRs) is crucial to reducing accidents in this sector. This paper is based on the data of 115 yachting tourism accidents in China’s coastal areas from 2008 to 2021. A fishbone diagram and the analytic hierarchy process (AHP) were used to identify the risk factors of yachting tourism from four aspects human, yachting, environmental, and management risk and to construct an evaluation index system. To perform dynamic evaluation, a dynamic evaluation model of YTSRs was built using dynamic Bayesian networks (DBN). The results indicate that human factors, such as the unsafe behavior of yachtsmen and tourists, are the primary risk factors; the risk is higher in summer than in winter, and the Pearl River Delta region has a greater risk of yachting tourism. It is suggested to improve the normal safety risk prevention and control system of yachting tourism, to advocate for multi-subject coordination and co-governance, and to improve the insurance service system so as to provide a guarantee for the safe and healthy development of yachting tourism in China. The findings provide theoretical and practical guidance for marine and coastal tourism safety management, as well as the prevention and control of YTSRs.

Suggested Citation

  • Yunhao Yao & Xiaoxing Zhou & Merle Parmak, 2023. "Risk assessment for yachting tourism in China using dynamic Bayesian networks," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-17, August.
    • Handle: RePEc:plo:pone00:0289607
    DOI: 10.1371/journal.pone.0289607
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0289607
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0289607&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0289607?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
    ---><---

    References listed on IDEAS

    as
    1. Christelle Viauroux & Ali Gungor, 2016. "An Empirical Analysis of Life Jacket Effectiveness in Recreational Boating," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 302-319, February.
    2. Meizhi Jiang & Jing Lu, 2020. "Maritime accident risk estimation for sea lanes based on a dynamic Bayesian network," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 649-664, July.
    3. Khan, Bushra & Khan, Faisal & Veitch, Brian & Yang, Ming, 2018. "An operational risk analysis tool to analyze marine transportation in Arctic waters," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 485-502.
    4. Alberto D. Dávila-Lamas & José J. Carbajal-Hernández & Luis P. Sánchez-Fernández & Virginia B. Niebla-Zatarain & César A. Hoil-Rosas, 2022. "Assessment of Coastal Locations Safety Using a Fuzzy Analytical Hierarchy Process-Based Model," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    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. Ziegler Haselein, Bruno & da Silva, Jonny Carlos & Hooey, Becky L., 2024. "Multiple machine learning modeling on near mid-air collisions: An approach towards probabilistic reasoning," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    2. 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).
    3. Carine Dominguez-Péry & Lakshmi Narasimha Raju Vuddaraju & Isabelle Corbett-Etchevers & Rana Tassabehji, 2021. "Reducing maritime accidents in ships by tackling human error: a bibliometric review and research agenda," Journal of Shipping and Trade, Springer, vol. 6(1), pages 1-32, December.
    4. Li, Huanhuan & Ren, Xujie & Yang, Zaili, 2023. "Data-driven Bayesian network for risk analysis of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Adland, Roar & Jia, Haiying & Lode, Tønnes & Skontorp, Jørgen, 2021. "The value of meteorological data in marine risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    6. Julio Ernesto Zaldivar-Herrera & Luis Pastor Sánchez-Fernández & Luis Manuel Rodríguez-Méndez, 2024. "Network Long-Term Evolution Quality of Service Assessment Using a Weighted Fuzzy Inference System," Mathematics, MDPI, vol. 12(24), pages 1-26, December.
    7. Li, Baode & Lu, Jing & Li, Jing & Zhu, Xuebin & Huang, Chuan & Su, Wan, 2022. "Scenario evolutionary analysis for maritime emergencies using an ensemble belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Zhongxiu Peng & Cong Wang & Wenqing Xu & Jinsong Zhang, 2022. "Research on Location-Routing Problem of Maritime Emergency Materials Distribution Based on Bi-Level Programming," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    9. Zhou, Jian-Lan & Lei, Yi, 2020. "A slim integrated with empirical study and network analysis for human error assessment in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    10. Li, Jue & Li, Heng & Wang, Fan & Cheng, Andy S.K. & Yang, Xincong & Wang, Hongwei, 2021. "Proactive analysis of construction equipment operators’ hazard perception error based on cognitive modeling and a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    11. Fan, Hanwen & Jia, Haiying & He, Xuzhuo & Lyu, Jing, 2024. "Navigating uncertainty: A dynamic Bayesian network-based risk assessment framework for maritime trade routes," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    12. Fu, Shanshan & Yu, Yuerong & Chen, Jihong & Xi, Yongtao & Zhang, Mingyang, 2022. "A framework for quantitative analysis of the causation of grounding accidents in arctic shipping," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    13. Yu, Qing & Liu, Kezhong & Chang, Chia-Hsun & Yang, Zaili, 2020. "Realising advanced risk assessment of vessel traffic flows near offshore wind farms," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    14. Koçak, Saim Turgut & Yercan, Funda, 2021. "Comparative cost-effectiveness analysis of Arctic and international shipping routes: A Fuzzy Analytic Hierarchy Process," Transport Policy, Elsevier, vol. 114(C), pages 147-164.
    15. Zarghami, Seyed Ashkan & Dumrak, Jantanee, 2021. "Unearthing vulnerability of supply provision in logistics networks to the black swan events: Applications of entropy theory and network analysis," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    16. Rajesh S. Prabhu Gaonkar & V. Mariappan, 2020. "Transportation time reliability appraisal in maritime context," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 736-746, June.
    17. Hossain, Niamat Ullah Ibne & Nur, Farjana & Hosseini, Seyedmohsen & Jaradat, Raed & Marufuzzaman, Mohammad & Puryear, Stephen M., 2019. "A Bayesian network based approach for modeling and assessing resilience: A case study of a full service deep water port," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 378-396.
    18. Hossain, Niamat Ullah Ibne & Jaradat, Raed & Hosseini, Seyedmohsen & Marufuzzaman, Mohammad & Buchanan, Randy K., 2019. "A framework for modeling and assessing system resilience using a Bayesian network: A case study of an interdependent electrical infrastructure system," International Journal of Critical Infrastructure Protection, Elsevier, vol. 25(C), pages 62-83.
    19. Deng, Wanyi & Ma, Xiaoxue & Qiao, Weiliang, 2024. "A novel methodology to quantify the impact of safety barriers on maritime operational risk based on a probabilistic network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    20. Zvyagina, Tatiana & Zvyagin, Petr, 2022. "A model of multi-objective route optimization for a vessel in drifting ice," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0289607. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.