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Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

Published online by Cambridge University Press:  30 July 2020

Remzi Fiskin Open the ORCID record for Remzi Fiskin [Opens in a new window] ,
Erkan Cakir  and
Coşkan Sevgili
Remzi Fiskin*
Affiliation:
(Department of Marine Transportation Engineering, Fatsa Faculty of Marine Sciences, Ordu University, Ordu, Turkey)
Erkan Cakir
Affiliation:
(Department of Marine Transportation Engineering, Maritime Faculty, Recep Tayyip Erdogan University, Rize, Turkey)
Coşkan Sevgili
Affiliation:
(Department of Marine Transportation Engineering, Maritime Faculty, Dokuz Eylül University, İzmir, Turkey)
*
(E-mail: remzi.fiskin@gmail.com)
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Abstract

As tugboats interact very closely with ships in restricted waters, the possibility of accidents increases in these operations. Despite the high accident possibility, there is a gap in studies on tugboat accidents. This study aims to analyse accidents involving tugboats using data mining. For this purpose, a tugboat accidents dataset consisting of a total of 496 accident records for the period from 2008 to 2019 was collected. Logistic regression and decision tree algorithms were implemented to the dataset. The results revealed that tugboat propulsion type is the most important and influential factor in the severity of tugboat accidents. The inferences drawn from these results could be beneficial for tugboat operators and port authorities in enhancing their awareness of the factors affecting tugboat accidents. In addition, the outputs of this study can be a reference for management units in developing strategies for preventing tugboat accidents and can also be used in effective planning for practicable prevention programmes and practices.

Keywords

TugboatsDecision TreeData MiningAccident Severity

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Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 1 , January 2021 , pp. 79 - 104
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Copyright
Copyright © The Royal Institute of Navigation 2020

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