IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/116490.html
   My bibliography  Save this paper

Shipping Inspections, Detentions, and Accidents: An Empirical Analysis of Risk Dimensions

Author

Listed:
  • Heij, C.
  • Knapp, S.

Abstract

Inspections play a key role in keeping vessels safe. Inspection authorities employ different policies to decide which vessels to inspect, including type of vessel, age, and flag. Attention for vessel history is usually restricted only to past detentions. This paper shows that it helps to combine past detention with past accident information to target risky vessels for inspection and to prevent serious and very serious accidents. Five methods are presented to classify risk of vessels based on these two risk dimensions, i.e., detention risk and accident risk, each of which involves an extensive set of risk factors. It is shown that these classification methods have predictive power for future serious and very serious accidents. Compared to using only detention information, incorporation of accident risk improves inspection hit rates for vessels with future accidents by 30-50%, depending on the applied inspection rate. It is recommended to focus on vessels where both risks are relatively high. A practical example shows management implications for inspection authorities how to prevent missing risky ships and how to prioritize inspection areas defined in terms of eight risk domains that include collisions, groundings, engine and hull failures, loss of life, fire, and pollution.

Suggested Citation

  • Heij, C. & Knapp, S., 2018. "Shipping Inspections, Detentions, and Accidents: An Empirical Analysis of Risk Dimensions," Econometric Institute Research Papers 2018-36, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:116490
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/116490/EI-Report-2018-36-Targeting-with-combined-risk-dimensions.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Z. L. Yang & J. Wang & K. X. Li, 2013. "Maritime safety analysis in retrospect," Maritime Policy & Management, Taylor & Francis Journals, vol. 40(3), pages 261-277, May.
    2. Perepelkin, Mihail & Knapp, Sabine & Perepelkin, German & de Pooter, Michiel, 2010. "An improved methodology to measure flag performance for the shipping industry," Marine Policy, Elsevier, vol. 34(3), pages 395-405, May.
    3. Pierre Cariou & Maximo Q. Mejia & Francois-Charles Wolff, 2007. "An econometric analysis of deficiencies noted in port state control inspections," Maritime Policy & Management, Taylor & Francis Journals, vol. 34(3), pages 243-258, June.
    4. Heij, C. & Knapp, S., 2018. "Predictive power of inspection outcomes for future shipping accidents," Econometric Institute Research Papers EI2018-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Christiaan Heij & Sabine Knapp, 2018. "Predictive power of inspection outcomes for future shipping accidents – an empirical appraisal with special attention for human factor aspects," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(5), pages 604-621, July.
    6. Sabine Knapp & Philip Hans Franses, 2007. "A global view on port state control: econometric analysis of the differences across port state control regimes," Maritime Policy & Management, Taylor & Francis Journals, vol. 34(5), pages 453-482, October.
    7. 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.
    8. Ji, Xichen & Brinkhuis, Jan & Knapp, Sabine, 2015. "A method to measure enforcement effort in shipping with incomplete information," Marine Policy, Elsevier, vol. 60(C), pages 162-170.
    9. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653, January.
    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. Knapp, S. & Franses, Ph.H.B.F. & B. Whitby (Bruce), 2020. "Measuring the effect of perceived corruption on detention and incident risk – an empirical analysis," Econometric Institute Research Papers EI 2020-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    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. Wang, Shuaian & Yan, Ran & Qu, Xiaobo, 2019. "Development of a non-parametric classifier: Effective identification, algorithm, and applications in port state control for maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 129-157.
    2. Cariou, Pierre & Wolff, Francois-Charles, 2015. "Identifying substandard vessels through Port State Control inspections: A new methodology for Concentrated Inspection Campaigns," Marine Policy, Elsevier, vol. 60(C), pages 27-39.
    3. Yan, Ran & Wang, Shuaian & Fagerholt, Kjetil, 2020. "A semi-“smart predict then optimize” (semi-SPO) method for efficient ship inspection," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 100-125.
    4. Chang, Chia-Hsun & Kontovas, Christos & Yu, Qing & Yang, Zaili, 2021. "Risk assessment of the operations of maritime autonomous surface ships," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    5. Xiao, Yi & Qi, Guanqiu & Jin, Mengjie & Yuen, Kum Fai & Chen, Zhuo & Li, Kevin X., 2021. "Efficiency of Port State Control inspection regimes: A comparative study," Transport Policy, Elsevier, vol. 106(C), pages 165-172.
    6. Fan, Lixian & Zhang, Meng & Yin, Jingbo & Zhang, Jinfen, 2022. "Impacts of dynamic inspection records on port state control efficiency using Bayesian network analysis," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    7. Wang, Yuhong & Zhang, Fan & Yang, Zhisen & Yang, Zaili, 2021. "Incorporation of deficiency data into the analysis of the dependency and interdependency among the risk factors influencing port state control inspection," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    8. Knapp, S. & van de Velden, M., 2021. "Exploration of machine learning algorithms for maritime risk applications," Econometric Institute Research Papers 2021-03, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. 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.
    10. Junjie Fu & Xinqiang Chen & Shubo Wu & Chaojian Shi & Huafeng Wu & Jiansen Zhao & Pengwen Xiong, 2020. "Mining ship deficiency correlations from historical port state control (PSC) inspection data," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-19, February.
    11. 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).
    12. Yang, Zaili & Ng, Adolf K.Y. & Wang, Jin, 2014. "A new risk quantification approach in port facility security assessment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 72-90.
    13. Wang, Likun & Yang, Zaili, 2018. "Bayesian network modelling and analysis of accident severity in waterborne transportation: A case study in China," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 277-289.
    14. Heij, C. & Knapp, S., 2014. "Effects of wind strength and wave height on ship incident risk: regional trends and seasonality," Econometric Institute Research Papers EI2014-15, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    15. 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).
    16. Esma Gül Emecen Kara, 2016. "Risk Assessment in the Istanbul Strait Using Black Sea MOU Port State Control Inspections," Sustainability, MDPI, vol. 8(4), pages 1-17, April.
    17. Lutz Kretschmann, 2020. "Leading indicators and maritime safety: predicting future risk with a machine learning approach," Journal of Shipping and Trade, Springer, vol. 5(1), pages 1-22, December.
    18. Liangxia Zhong & Jiaxin Wu & Yiqing Wen & Bingjie Yang & Manel Grifoll & Yunping Hu & Pengjun Zheng, 2023. "Analysis of Factors Affecting the Effectiveness of Oil Spill Clean-Up: A Bayesian Network Approach," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    19. Knapp, S. & Heij, C., 2019. "Improved strategies for the maritime industry to target vessels for inspection and to select inspection priority areas," Econometric Institute Research Papers EI2019-21, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    20. Armando Graziano & Pierre Cariou & François-Charles Wolff & Maximo Mejia & Jens-Uwe Schröder-Hinrichs, 2017. "Port state control inspections in the European Union: Do inspector's number and background matter?," Working Papers halshs-01649418, HAL.

    More about this item

    Keywords

    Maritime safety; inspection policy; vessel-specific risk; detention risk; accident risk; risk domains;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ems:eureir:116490. 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: RePub The email address of this maintainer does not seem to be valid anymore. Please ask RePub to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    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.