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Leading indicators and maritime safety: predicting future risk with a machine learning approach

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  • Lutz Kretschmann

    (Fraunhofer Center for Maritime Logistics and Services CML)

Abstract

The shipping industry has been quite successful in reducing the number of major accidents in the past. In order to continue this development in the future, innovative leading risk indicators can make a significant contribution. If designed properly, they enable a forward-looking identification and assessment of existing risks for ship and crew, which in turn allows the implementation of mitigating measures before adverse events occur. Right now, the opportunity for developing such leading risk indicators is positively influenced by the ongoing digital transformation in the maritime industry. With an increasing amount of data from ship operation becoming available, these can be exploited in innovative risk management solutions. By combining the idea of leading risk indicators with data and algorithm-based risk management methods, this paper firstly establishes a development framework for designing maritime risk models based on safety-related data collected onboard. Secondly, the development framework is applied in a proof of concept where an innovative machine learning-based approach is used to calculate a leading maritime risk indicator. Overall, findings confirm that a data- and algorithm-based approach can be used to determine a leading risk indicator per ship, even though the achieved model performance is not yet regarded as satisfactory and further research is planned.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:josatr:v:5:y:2020:i:1:d:10.1186_s41072-020-00071-1
    DOI: 10.1186/s41072-020-00071-1
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    References listed on IDEAS

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    1. Knapp, S., 2013. "An integrated risk estimation methodology: Ship specific incident type risk," Econometric Institute Research Papers EI 2013-11, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Maher Maalouf & Theodore B. Trafalis, 2011. "Rare events and imbalanced datasets: an overview," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 3(4), pages 375-388.
    3. 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.
    4. 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.
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    Cited by:

    1. Munim, Ziaul Haque & Sørli, Michael André & Kim, Hyungju & Alon, Ilan, 2024. "Predicting maritime accident risk using Automated Machine Learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    2. Krzysztof Wróbel & Mateusz Gil & Przemysław Krata & Karol Olszewski & Jakub Montewka, 2023. "On the use of leading safety indicators in maritime and their feasibility for Maritime Autonomous Surface Ships," Journal of Risk and Reliability, , vol. 237(2), pages 314-331, April.
    3. François Fulconis & Raphael Lissillour, 2021. "Toward a behavioral approach of international shipping: a study of the inter-organisational dynamics of maritime safety," Journal of Shipping and Trade, Springer, vol. 6(1), pages 1-23, December.

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