IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v94y2009i8p1349-1357.html
   My bibliography  Save this article

Analysis of the marine traffic safety in the Gulf of Finland

Author

Listed:
  • Kujala, P.
  • Hänninen, M.
  • Arola, T.
  • Ylitalo, J.

Abstract

The Gulf of Finland (GOF) is geographically situated between Finland and Estonian waters. The seafloor varies between deep and shallow and a number of underwater rocks exist in the Finnish archipelago area. The marine traffic has been growing fast during the last years in this area, especially due to the rapid increase of the transportation of various cargoes to Russia and the transport of oil from Russia. In this paper the safety of the marine traffic in the GOF area is analysed. First a detail accident statistics during the last 10 years are described and thereafter the risk of ship collisions is studied by theoretical modelling in two locations. Finally the results of the theoretical models are compared with actual accident statistics. The results reveal that grounding is the dominating accident type in these waters and typically about 11 groundings take place annually, of which about one is a tanker grounding. For collision the highest risks are caused by the passenger ship/RoPax ships traffic between Helsinki and Tallinn together with the high traffic intensity eastwards/westward to and from Russian harbours. The theoretical collision models give good results when compared with the accident statistics. AIS data is utilised in the theoretical models to calculate the geometric collision probabilities.

Suggested Citation

  • Kujala, P. & Hänninen, M. & Arola, T. & Ylitalo, J., 2009. "Analysis of the marine traffic safety in the Gulf of Finland," Reliability Engineering and System Safety, Elsevier, vol. 94(8), pages 1349-1357.
  • Handle: RePEc:eee:reensy:v:94:y:2009:i:8:p:1349-1357
    DOI: 10.1016/j.ress.2009.02.028
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832009000568
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2009.02.028?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. Trucco, P. & Cagno, E. & Ruggeri, F. & Grande, O., 2008. "A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 845-856.
    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. Wu, Bing & Yip, Tsz Leung & Yan, Xinping & Guedes Soares, C., 2022. "Review of techniques and challenges of human and organizational factors analysis in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    2. Yang, Zhisen & Yang, Zaili & Yin, Jingbo, 2018. "Realising advanced risk-based port state control inspection using data-driven Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 38-56.
    3. Yan-Feng Li & Jinhua Mi & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Dynamic fault tree analysis based on continuous-time Bayesian networks under fuzzy numbers," Journal of Risk and Reliability, , vol. 229(6), pages 530-541, December.
    4. Li, Huanhuan & Çelik, Cihad & Bashir, Musa & Zou, Lu & Yang, Zaili, 2024. "Incorporation of a global perspective into data-driven analysis of maritime collision accident risk," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    5. Afshin Ghahramani & John McLean Bennett & Aram Ali & Kathryn Reardon-Smith & Glenn Dale & Stirling D. Roberton & Steven Raine, 2021. "A Risk-Based Approach to Mine-Site Rehabilitation: Use of Bayesian Belief Network Modelling to Manage Dispersive Soil and Spoil," Sustainability, MDPI, vol. 13(20), pages 1-23, October.
    6. Qiao, Wanguan, 2021. "Analysis and measurement of multifactor risk in underground coal mine accidents based on coupling theory," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    7. Sotiralis, P. & Ventikos, N.P. & Hamann, R. & Golyshev, P. & Teixeira, A.P., 2016. "Incorporation of human factors into ship collision risk models focusing on human centred design aspects," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 210-227.
    8. Elon Manurung & Effrida Effrida & Andreas James Gondowonto, 2019. "Effect of Financial Performance, Good Corporate Governance and Corporate Size on Corporate Value in Food and Beverages," International Journal of Economics and Financial Issues, Econjournals, vol. 9(6), pages 100-105.
    9. Bing Wu & Huibin Tian & Xinping Yan & C. Guedes Soares, 2020. "A probabilistic consequence estimation model for collision accidents in the downstream of Yangtze River using Bayesian Networks," Journal of Risk and Reliability, , vol. 234(2), pages 422-436, April.
    10. Montewka, Jakub & Hinz, Tomasz & Kujala, Pentti & Matusiak, Jerzy, 2010. "Probability modelling of vessel collisions," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 573-589.
    11. Su-Hyung Kim & Kyung-Jin Ryu & Seung-Hyun Lee & Kyoung-Hoon Lee & Seong-Hun Kim & Yoo-Won Lee, 2023. "Enhancing Sustainability through Analysis and Prevention: A Study of Fatal Accidents on Trap Boats within the Commercial Fishing Industry," Sustainability, MDPI, vol. 15(21), pages 1-23, October.
    12. Moglia, Magnus & Alexander, Kim S. & Thephavanh, Manithaythip & Thammavong, Phomma & Sodahak, Viengkham & Khounsy, Bountom & Vorlasan, Sysavanh & Larson, Silva & Connell, John & Case, Peter, 2018. "A Bayesian network model to explore practice change by smallholder rice farmers in Lao PDR," Agricultural Systems, Elsevier, vol. 164(C), pages 84-94.
    13. Özkan Uğurlu & Serdar Yıldız & Sean Loughney & Jin Wang & Shota Kuntchulia & Irakli Sharabidze, 2020. "Analyzing Collision, Grounding, and Sinking Accidents Occurring in the Black Sea Utilizing HFACS and Bayesian Networks," Risk Analysis, John Wiley & Sons, vol. 40(12), pages 2610-2638, December.
    14. Pandya, Dhruv & Podofillini, Luca & Emert, Frank & Lomax, Antony J. & Dang, Vinh N. & Sansavini, Giovanni, 2020. "Quantification of a human reliability analysis method for radiotherapy applications based on expert judgment aggregation," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
    15. Maria Cieśla & Elżbieta Macioszek, 2022. "The Perspective Projects Promoting Sustainable Mobility by Active Travel to School on the Example of the Southern Poland Region," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    16. Sezer, Sukru Ilke & Camliyurt, Gokhan & Aydin, Muhmmet & Akyuz, Emre & Gardoni, Paolo, 2023. "A bow-tie extended D-S evidence-HEART modelling for risk analysis of cargo tank cracks on oil/chemical tanker," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    17. Beatriz Molina Serrano & Nicoleta González-Cancelas & Francisco Soler-Flores & Samir Awad-Nuñez & Alberto Camarero Orive, 2018. "Use of Bayesian Networks to Analyze Port Variables in Order to Make Sustainable Planning and Management Decision," Logistics, MDPI, vol. 2(1), pages 1-16, January.
    18. Kriaa, Siwar & Pietre-Cambacedes, Ludovic & Bouissou, Marc & Halgand, Yoran, 2015. "A survey of approaches combining safety and security for industrial control systems," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 156-178.
    19. 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).
    20. F De Carlo & O Borgia & M Tucci, 2011. "Risk-based inspections enhanced with Bayesian networks," Journal of Risk and Reliability, , vol. 225(3), pages 375-386, September.

    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:reensy:v:94:y:2009:i:8:p:1349-1357. 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: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.