Big data analysis of port state control ship detention database

Abstract

This study uses big data analysis to examine the relationships between detention deficiencies and external factors as well as between detention deficiencies themselves. Data are taken from the ship detentions database that has been accumulatively published from Port State Control inspections, which have been executed for many years in the member authorities of the Tokyo Memorandum of Understanding. Each factor of the PSC detention database is analyzed, with additional preprocessing, to explore the potential regularity of ship detention deficiencies. The results show that using association rule mining techniques in big data analysis can accurately and objectively mine the regularity correlation between ship detention deficiencies, as well as between these deficiencies and related factors. The techniques can provide countermeasures and be used as a reference by ship management personnel during the corresponding PSC inspection, reducing the detention rate of ships. This can provide a more targeted method to be adopted by the maritime authority in the practical work of inspections. By using this method, the working efficiency of staff members can be significantly improved, reducing the adverse influences brought to navigation safety and the marine environment due to sub-standard vessels.