Development of a non-parametric classifier: Effective identification, algorithm, and applications in port state control for maritime transportation
Journal article, 2019

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.

Maritime safety

Maritime transportation

Port state control (PSC)

TAN classifier

Bayesian network (BN)

Author

Shuaian Wang

Hong Kong Polytechnic University

Ran Yan

Hong Kong Polytechnic University

Xiaobo Qu

Chalmers, Architecture and Civil Engineering, GeoEngineering

Transportation Research Part B: Methodological

0191-2615 (ISSN)

Vol. 128 129-157

Subject Categories

Other Computer and Information Science

Other Engineering and Technologies not elsewhere specified

Bioinformatics (Computational Biology)

DOI

10.1016/j.trb.2019.07.017

More information

Latest update

11/7/2019