IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v235y2023ics0951832023001369.html
   My bibliography  Save this article

A framework estimating the minimum sample size and margin of error for maritime quantitative risk analysis

Author

Listed:
  • Puisa, Romanas
  • Montewka, Jakub
  • Krata, Przemyslaw

Abstract

The average accident frequency is essential for quantitative risk analysis and is conventionally estimated from accident statistics. This paper has systematically synthesised the knowledge on statistical errors and offered the missing instructions, a framework, for determining the minimum sample size and the margin of error (MOE) when calculating the average accident frequency from an accident database at hand. We have applied this framework to representative accident datasets in the maritime domain and presented the revealing results that can already be used in QRAs based on these datasets. The findings are useful to both QRA analysts and policy makers. Interestingly, the framework application has revealed that the determined minimum sample sizes would exceed the datasets available in existing maritime casualty databases by decades, requiring at least 10% MOE to be factored into pertinent QRAs. By the same token, the earlier notable QRAs (developed as part of formal safety assessments in support of rule making) had to consider the MOE of over 30%, given the sample sizes used, likely shifting the conclusions they arrived at. Other findings of the application have shown that the average accident frequencies for large passenger ships have remained constant over the past 40 years.

Suggested Citation

  • Puisa, Romanas & Montewka, Jakub & Krata, Przemyslaw, 2023. "A framework estimating the minimum sample size and margin of error for maritime quantitative risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  • Handle: RePEc:eee:reensy:v:235:y:2023:i:c:s0951832023001369
    DOI: 10.1016/j.ress.2023.109221
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023001369
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109221?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. Du, Lei & Goerlandt, Floris & Kujala, Pentti, 2020. "Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    2. Izdebski, Mariusz & Jacyna-Gołda, Ilona & Gołda, Paweł, 2022. "Minimisation of the probability of serious road accidents in the transport of dangerous goods," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Taleb, Nassim Nicholas, 2019. "How much data do you need? An operational, pre-asymptotic metric for fat-tailedness," International Journal of Forecasting, Elsevier, vol. 35(2), pages 677-686.
    4. Jacob Anhøj & Anne Vingaard Olesen, 2014. "Run Charts Revisited: A Simulation Study of Run Chart Rules for Detection of Non-Random Variation in Health Care Processes," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-13, November.
    5. Jason R. W. Merrick & Rene Van Dorp, 2006. "Speaking the Truth in Maritime Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 26(1), pages 223-237, February.
    6. Mazurek, J. & Lu, L. & Krata, P. & Montewka, J. & Krata, H. & Kujala, P., 2022. "An updated method identifying collision-prone locations for ships. A case study for oil tankers navigating in the Gulf of Finland," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Zhang, Mingyang & Kujala, Pentti & Hirdaris, Spyros, 2022. "A machine learning method for the evaluation of ship grounding risk in real operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    8. Kvaløy, J.T. & Aven, T., 2005. "An alternative approach to trend analysis in accident data," Reliability Engineering and System Safety, Elsevier, vol. 90(1), pages 75-82.
    9. Zhang, Jinfen & Wan, Chengpeng & He, Anxin & Zhang, Di & Soares, C. Guedes, 2021. "A two-stage black-spot identification model for inland waterway transportation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    10. Kaptan, Mehmet & Uğurlu, Özkan & Wang, Jin, 2021. "The effect of nonconformities encountered in the use of technology on the occurrence of collision, contact and grounding accidents," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    11. Montewka, Jakub & Goerlandt, Floris & Kujala, Pentti, 2014. "On a systematic perspective on risk for formal safety assessment (FSA)," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 77-85.
    12. Vanem, Erik & Antão, Pedro & Østvik, Ivan & de Comas, Francisco Del Castillo, 2008. "Analysing the risk of LNG carrier operations," Reliability Engineering and System Safety, Elsevier, vol. 93(9), pages 1328-1344.
    13. Mauro, Francesco & Vassalos, Dracos & Paterson, Donald, 2022. "Critical damages identification in a multi-level damage stability assessment framework for passenger ships," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    14. Ung, S.T., 2021. "Navigation Risk estimation using a modified Bayesian Network modeling-a case study in Taiwan," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    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. Ding, Wenzhe & Bai, Xiang & Wang, Qingwei & Long, Fang & Li, Hailin & Wu, Zhengrong & Liu, Jian & Yao, Huisheng & Yang, Hong, 2024. "A truncated test scheme design method for success-failure in-orbit tests," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Li, Guoqi & Pu, Gang & Yang, Jiaxin & Jiang, Xinguo, 2024. "A multidimensional quantitative risk assessment framework for dense areas of stay points for urban HazMat vehicles," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Saud Almutairi & Mudthir Bakri & Abdullatif A. AlMunifi & Mohammed Algahtany & Saud Aldalbahy, 2023. "The Status of the Saudi Construction Industry during the COVID-19 Pandemic," Sustainability, MDPI, vol. 15(21), pages 1-21, October.
    4. Fan, Cunlong & Montewka, Jakub & Bolbot, Victor & Zhang, Yang & Qiu, Yuhui & Hu, Shenping, 2024. "Towards an analysis framework for operational risk coupling mode: A case from MASS navigating in restricted waters," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    5. Zhou, Kaiwen & Xing, Wenbin & Wang, Jingbo & Li, Huanhuan & Yang, Zaili, 2024. "A data-driven risk model for maritime casualty analysis: A global perspective," Reliability Engineering and System Safety, Elsevier, vol. 244(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. Antão, P. & Sun, S. & Teixeira, A.P. & Guedes Soares, C., 2023. "Quantitative assessment of ship collision risk influencing factors from worldwide accident and fleet data," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Ruponen, Pekka & Montewka, Jakub & Tompuri, Markus & Manderbacka, Teemu & Hirdaris, Spyros, 2022. "A framework for onboard assessment and monitoring of flooding risk due to open watertight doors for passenger ships," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Cai, Mingyou & Zhang, Jinfen & Zhang, Di & Yuan, Xiaoli & Soares, C. Guedes, 2021. "Collision risk analysis on ferry ships in Jiangsu Section of the Yangtze River based on AIS data," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. 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).
    6. Mauro, Francesco & Vassalos, Dracos & Paterson, Donald, 2022. "Critical damages identification in a multi-level damage stability assessment framework for passenger ships," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    7. Szlapczynski, Rafal & Szlapczynska, Joanna, 2021. "A ship domain-based model of collision risk for near-miss detection and Collision Alert Systems," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. 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).
    9. Gil, Mateusz & Kozioł, Paweł & Wróbel, Krzysztof & Montewka, Jakub, 2022. "Know your safety indicator – A determination of merchant vessels Bow Crossing Range based on big data analytics," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    10. 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).
    11. Yang, Zhisen & Wan, Chengpeng & Yang, Zaili & Yu, Qing, 2021. "Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    12. Zhang, Mingyang & Zhang, Di & Fu, Shanshan & Kujala, Pentti & Hirdaris, Spyros, 2022. "A predictive analytics method for maritime traffic flow complexity estimation in inland waterways," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    13. Mazurek, J. & Lu, L. & Krata, P. & Montewka, J. & Krata, H. & Kujala, P., 2022. "An updated method identifying collision-prone locations for ships. A case study for oil tankers navigating in the Gulf of Finland," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    14. Sarhadi, Hassan & Naoum-Sawaya, Joe & Verma, Manish, 2020. "A robust optimization approach to locating and stockpiling marine oil-spill response facilities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    15. Sezer, Sukru Ilke & Akyuz, Emre & Arslan, Ozcan, 2022. "An extended HEART Dempster–Shafer evidence theory approach to assess human reliability for the gas freeing process on chemical tankers," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    16. Martins, Marcelo Ramos & Maturana, Marcos Coelho, 2013. "Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 89-109.
    17. HÃ¥vold, Jon Ivar, 2010. "Safety culture and safety management aboard tankers," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 511-519.
    18. Wang, Lei & Liu, Qing & Dong, Shiyu & Guedes Soares, C., 2022. "Selection of countermeasure portfolio for shipping safety with consideration of investment risk aversion," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    19. Krata, Przemyslaw & Jachowski, Jacek, 2021. "Towards a modification of a regulatory framework aiming at bunker oil spill prevention from ships – A design aspect of bunker tanks vents location guided by CFD simulations," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    20. 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).

    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:235:y:2023:i:c:s0951832023001369. 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.