Enhancing data quality in maritime transportation: A practical method for imputing missing ship static data

Sun, Ruikai, Abouarghoub, Wessam ORCID: https://orcid.org/0000-0002-1647-1291 and Demir, Emrah ORCID: https://orcid.org/0000-0002-4726-2556 2025. Enhancing data quality in maritime transportation: A practical method for imputing missing ship static data. Ocean Engineering 315 , 119722. 10.1016/j.oceaneng.2024.119722

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Abstract

Maritime transport research, including emissions estimation, shipping network design, and bunker management relies on complete high-quality data. Incomplete ship static data often lead to bias and misleading conclusions. Existing imputation methods either depend on small data samples with poor imputation accuracy or are overly complex with practical limitations. This study addresses these issues by evaluating existing methods and proposing a new imputation approach to enhance the quality of ship static data. First, we introduce a stepwise multiple nonlinear regression method to simplify the imputation process and improve accuracy. Second, we propose a novel evaluation metric, the coverage rate, to assess the model performance. Finally, from a total of 14 models, we use a decision matrix to select the optimal model for imputing missing ship static data. These models are applied to a real dataset with missing values and cross-validated using multiple databases to ensure robustness. The proposed method maximizes the coverage rate, approaching nearly 100 percent for missing data. The most significant improvement was observed in main engine RPM imputation, where the average adjusted R-squared increased by at least 20.74%. Based on a large training dataset of 38,018 ships, this method can be directly applied to other maritime transport studies.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Start Date: 2024-11-17
Publisher:	Elsevier
ISSN:	0029-8018
Date of First Compliant Deposit:	25 November 2024
Date of Acceptance:	3 November 2024
Last Modified:	05 Dec 2024 17:06
URI:	https://orca.cardiff.ac.uk/id/eprint/174276

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