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

Jae Hun Kim, Juyeon Kim, Gunwoo Lee and Juneyoung Park
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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

Sustainability, 2021, vol. 13, issue 16, 1-14

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.

Keywords: container port; machine learning; accident prediction model; neural network; random forest; gradient boosting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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