Abstract
Ship detention serves as an obligatory but efficient manner in port state control (PSC) inspection, and accurate ship detention prediction provides early warning information for maritime traffic participants. Previous studies mainly focused on exploiting the relationship between ship factors (i.e., ship age and ship type) and PSC inspection reports. Less attention was paid to identify and predict the correlation between ship fatal deficiency and ship detention event. To address the issue, we propose a novel framework to identify crucial ship deficiency types with an optimized analytic hierarchy process (AHP) model. Then, the Naïve Bayes model is introduced to predict the ship detention probability considering weights of the identified crucial ship deficiency types. Finally, we evaluate our proposed model performance on the empirical PSC inspection dataset. The research findings can help PSC officials easily determine main ship deficiencies, and thus, less time cost is required for implementing the PSC inspection procedure. In that manner, the PSC officials can quickly make ship detention decision and thus enhance maritime traffic safety.
Highlights
Port state control (PSC) inspection aims at identifying various potential maritime traffic safety risks, and the ships with fatal deficiencies are detained to avoid potential maritime traffic accidents [1, 2]
Ough the PSC regime may vary in different countries, the PSC inspection regimes share the following points [6, 7]: (1) PSC officials inspect ships at lower risk with a larger time interval, and vice versa; (2) the PSC inspection procedure focuses on various factors, which may impose a potential but significant threat to maritime traffic safety [8, 9]. e ship detention decision is made after thoroughly inspecting the ship, which considers the weights of various observed deficiency factors. e empirical PSC inspection practice indicates that exploiting the close relationship between crucial deficiencies and ship detention can provide guidelines for quickly making ship detention decision
Our contributions can be summarized as follows: (1) we analyzed the weakness of the current PSC inspection methods in the real-world applications from the perspective of quantitative measurements; (2) to address the issue, we proposed a hybrid framework with Naıve Bayes model and an optimized analytic hierarchy process (AHP) model to transform the empirical PSC knowledge into common rules; (3) we verified the proposed framework performance via the empirical PSC inspection data, which were implemented in both qualitative and quantitative manners. e remainder of the paper is organized as follows
Summary
Port state control (PSC) inspection aims at identifying various potential maritime traffic safety risks, and the ships with fatal deficiencies are detained to avoid potential maritime traffic accidents [1, 2]. Shipping company is forced to completely correct the deficiencies for the detained ship, which are identified in the PSC inspection reports Both the PSC officials and shipping company hope to accurately determine ship deficiency, and further measurements will be taken to improve maritime traffic safety and efficiency [3,4,5]. Many researchers focused on identifying the key factors, which impose important influences on ship detention decision [10,11,12,13,14,15] They attempted to exploit the correlation between the critical factors (i.e., ship age, ship type, and number of deficiencies) and ship detention rate from large-scale PSC inspection data, which
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