Development of a non-parametric classifier: Effective identification, algorithm, and applications in port state control for maritime transportation

Shuaian Wang, Ran Yan, Xiaobo Qu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

26 Citations (Scopus)

Abstract

Maritime transportation plays a pivotal role in the economy and globalization, while it poses threats and risks to the maritime environment. In order to maintain maritime safety, one of the most important mitigation solutions is the Port State Control (PSC) inspection. In this paper, a data-driven Bayesian network classifier named Tree Augmented Naive Bayes (TAN) classifier is developed to identify high-risk foreign vessels coming to the PSC inspection authorities. By using data on 250 PSC inspection records from Hong Kong port in 2017, we construct the structure and quantitative parts of the TAN classifier. Then the proposed classifier is validated by another 50 PSC inspection records from the same port. The results show that, compared with the Ship Risk Profile selection scheme that is currently implemented in practice, the TAN classifier can discover 130% more deficiencies on average. The proposed classifier can help the PSC authorities to better identify substandard ships as well as to allocate inspection resources.

Original languageEnglish
Pages (from-to)129-157
Number of pages29
JournalTransportation Research Part B: Methodological
Volume128
DOIs
Publication statusPublished - Oct 2019

Keywords

  • Bayesian network (BN)
  • Maritime safety
  • Maritime transportation
  • Port state control (PSC)
  • TAN classifier

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation

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