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Predictive power of inspection outcomes for future shipping accidents

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  • Heij, C.
  • Knapp, S.

Abstract

This paper investigates whether deficiencies detected during port state control (PSC) inspections have predictive power for future accident risk, in addition to other vessel-specific risk factors like ship type, age, size, flag, and owner. The empirical analysis links accidents to past inspection outcomes and is based on data from all around the globe of PSC regimes using harmonized deficiency codes. These codes are aggregated into eight groups related to human factor aspects like crew qualifications, working and living conditions, and fatigue and safety management. This information is integrated by principal components into a single overall deficiency index, which is related to future accident risk by means of logit models. The factor by which accident risk increases for vessels with above average compared to below average deficiency scores is about 6 for total loss, 2 for very serious, 1.5 for serious, and 1.3 for less-serious accidents. Relations between deficiency scores and accident risk are presented in graphical format. The results may be of interest to PSC authorities for targeting inspection areas, to maritime administrations for improving asset allocation based on prediction scenarios connected with vessel traffic data, and to maritime insurers for refining their premium strategies.

Suggested Citation

  • 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.
    • Handle: RePEc:ems:eureir:105080
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    References listed on IDEAS

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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. 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).
    3. 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.
    4. 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).
    5. 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).
    6. 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.
    7. 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.

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    Keywords

    deficiencies; human factor; Maritime safety; port state control inspections; risk prediction; shipping accidents;
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