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Shipping inspections, detentions, and incidents: an empirical analysis of risk dimensions

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  • Christiaan Heij
  • Sabine Knapp

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 demonstrates that the correlation between the probabilities of detention and (very serious and serious) incidents is very low and that proactive prevention of future incidents is improved by accounting for both risk dimensions, that is, by combining past incident and detention information for targeting high-risk vessels for inspection. Five combined methods are presented to classify vessels based on these two risk dimensions, each of which involves extensive sets of factors. These combined classification methods have predictive power for future incidents. Depending on the applied inspection rate, incorporation of incident risk improves inspection hit rates for vessels with future incidents by 30–50% compared to using only detention information. It is recommended to focus on vessels where both risks are relatively high. A practical example shows how the methods can be applied for inspection selection and for prioritizing 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

  • Christiaan Heij & Sabine Knapp, 2019. "Shipping inspections, detentions, and incidents: an empirical analysis of risk dimensions," Maritime Policy & Management, Taylor & Francis Journals, vol. 46(7), pages 866-883, October.
  • Handle: RePEc:taf:marpmg:v:46:y:2019:i:7:p:866-883
    DOI: 10.1080/03088839.2019.1647362
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    Cited by:

    1. 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.
    2. 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).
    3. Yan, Ran & Wang, Shuaian & Zhen, Lu, 2023. "An extended smart “predict, and optimize” (SPO) framework based on similar sets for ship inspection planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    4. 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.
    5. 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.
    6. Yan, Ran & Liu, Yan & Wang, Shuaian, 2024. "A data-driven optimization approach to improving maritime transport efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 180(C).
    7. Yan, Ran & Wang, Shuaian & Cao, Jiannong & Sun, Defeng, 2021. "Shipping Domain Knowledge Informed Prediction and Optimization in Port State Control," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 52-78.
    8. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).

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