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Machine Learning-Based Models for Accident Prediction at a Korean Container Port

Author

Listed:
  • Jae Hun Kim

    (Department of Transportation & Logistics Engineering, Hanyang University, Ansan 15588, Korea)

  • Juyeon Kim

    (Department of Transportation & Logistics Engineering, Hanyang University, Ansan 15588, Korea)

  • Gunwoo Lee

    (Department of Transportation & Logistics Engineering, Hanyang University, Ansan 15588, Korea)

  • Juneyoung Park

    (Department of Transportation & Logistics Engineering, Hanyang University, Ansan 15588, Korea)

Abstract

The occurrence of accidents at container ports results in damages and economic losses in the terminal operation. Therefore, it is necessary to accurately predict accidents at container ports. Several machine learning models have been applied to predict accidents at a container port under various time intervals, and the optimal model was selected by comparing the results of different models in terms of their accuracy, precision, recall, and F1 score. The results show that a deep neural network model and gradient boosting model with an interval of 6 h exhibits the highest performance in terms of all the performance metrics. The applied methods can be used in the predicting of accidents at container ports in the future.

Suggested Citation

  • Jae Hun Kim & Juyeon Kim & Gunwoo Lee & Juneyoung Park, 2021. "Machine Learning-Based Models for Accident Prediction at a Korean Container Port," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9137-:d:614945
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    References listed on IDEAS

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    1. H. Rosoff & D. Von Winterfeldt, 2007. "A Risk and Economic Analysis of Dirty Bomb Attacks on the Ports of Los Angeles and Long Beach," Risk Analysis, John Wiley & Sons, vol. 27(3), pages 533-546, June.
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    Cited by:

    1. Mahdi Jahangard & Ying Xie & Yuanjun Feng, 2025. "Leveraging machine learning and optimization models for enhanced seaport efficiency," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 27(4), pages 710-751, December.
    2. 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).
    3. Aleksandar Aleksić & Milan Ranđelović & Dragan Ranđelović, 2023. "Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    4. Ousman Khan & Jimoh Olawale Ajadi & M Pear Hossain, 2024. "Predicting malaria outbreak in The Gambia using machine learning techniques," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-22, May.
    5. Susantha Wanniarachchi & Ranjan Sarukkalige & H. A. Prasantha Hapuarachchi & Pattiyage I.A. Gomes & Upaka Rathnayake, 2026. "Uncertainty Reduction in Near Real-time Satellite Precipitation Estimates by Integrating Soil Moisture and Potential Evapotranspiration Using a Machine Learning Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 40(5), pages 1-20, March.
    6. Wen‐Kai K. Hsu & Show‐Hui S. Huang & Nguyen Tan Huynh & Kuan‐Han Huang, 2024. "An evaluation model of sustainable efficiency for container terminals," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(1), pages 1170-1187, February.

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