Bayesian Network modelling of Port State Control Inspections

The Port State Control (PSC) regimes have been developed after several important maritime accidents allowing countries to inspect foreign-registered ships in port other than those of the flag state and take action against ships that are not in compliance with international rules. In the present research, the risk influencing factors adopted in the definition of the Ship Risk Profile used for selecting ships for inspections under the New Inspection Regime of the Paris Memorandum of Understanding (MoU) on Port the State Control are characterized. Moreover, the PSC inspections at two ports, Thessaloniki and Liverpool ports, are analyzed in terms of type and age of ships and other factors influencing the ships’ risk. In addition, the results of inspections in terms of detentions and number and type of deficiencies found at the two ports are analyzed. Finally, four Bayesian network models are developed using the data from Port State Control inspections. Two models are used to analyze the Thessaloniki port and two models the Liverpool port. The first two Bayesian network models, one for each port, are used to assess how risk factors such as the flag, the age, the recognized organization among others, influence the number of deficiencies and the detention of the ship. The other two models focus on the categories of deficiencies and how the risk factors influence specific deficiencies. Guedes Soares and Teixeira (2001) have reviewed some applications of quantified risk assessment within the maritime transportation sector and identified early studies on the probability of ship loss by foundering and capsizing. However, many frameworks have been developed for assessing risks in maritime industry with both quantitative and qualitative approaches. Montewka et al. (2014) introduced a systematic, transferable and proactive framework estimating the risk for maritime transportation systems. Their framework is developed with the use of Bayesian Belief Networks and combines discrete and continuous variables. Moreover, Chai et al. (2017) developed a Quantitative Risk Assessment (QRA) model in order to evaluate the risk of ship collisions. Since PSC is the main strategy to eliminate substandard ships, more and more research works have addressed this subject. The literature on Port State Control is broad and covers many aspects such as elements of law, effectiveness, targeting, and discrepancies in implementation among others. Sage (2005) proposed a criterion for the identification of ‘High Risk Vessels’ (HRV) in European waters that would allow coastal States to monitor the movements of ships posing significant risk. The idea was to develop a risk index, which could be calculated and attached to ships individually. Cariou et al. (2009) investigated how the different target factors contribute to deficiencies and detentions of the vessel using data from 26.515 PSC inspections that took place within the Indian Ocean MoU region from 2002 to 2006. Cariou and Wolff (2015) proposed a methodology to complement existing practices focusing on detention with the objective to improve the selection process. They concluded that current practices should still rely on detention statistics but could be improved through a combination of quintile regressions for count data and Probit models. Graziano et al. (2017) conducted a study to determine the factors leading to differences in treatment during the Port State Control inspection process among EU Member States using data from 14 elite interviews. According to them, Paris MOU is the most effective of the regional agreements. However, a single training policy for Port State Control Officers will reduce the differences from country to country during the inspection. Moreover, Graziano et al. (2018) analyzed 25 inspection reports prepared by EMSA to monitor the level of implementation and enforcement of the Directive 2009/16/EC on Port State Control. Chen et al. (2019) analyzed the factors behind the detention of ships using ship detention 2008–2017 data from port states in the Asia-Pacific Region collected by Tokyo MOU. The factors considered are not ship-related (type, age, flag etc) but the types of deficiencies obtained from Tokyo MOU annual reports Bayesian Networks (BNs) is a common tool used in several research works to develop frameworks for risk assessment. The capability of representing rather complex, not necessarily causal but uncertain relationships makes Bayesian Networks a powerful tool for risk modelling. Bayesian Network models can be developed directly from data via expert elicitation or a combination of both. The data-driven approach reduces the dependence on human experts and in some cases increases the accuracy of the model Hänninen (2014) used Bayesian networks for maritime safety modelling. They showed that the most important advantages of BNs are the easy combination of data with expert knowledge and the capability of updating the model with new information and of propagating evidences related to specific scenarios. Wang et al. (2012) presented an innovative approach towards integrating logistic regression and a BN. In their model they used a binary logistic regression method of providing input for a BN, making use of different maritime accident data resources. Hänninen and Kujala (2014) explored the dependencies of deficiencies and detentions of the ship and ship’s involvement in accidents and incidents. They used Bayesian networks with the method of learning from the inspection, accident and incident data. Sotiralis et al. (2016) presented an approach that more adequately incorporates human factors considerations into quantitative risk analysis of ship operation. The approach is based on the development of a Bayesian Network model that take into account the human performance and calculates the collision accidental probability due to human error. Yang et al. (2018a) proposed a data-driven Bayesian Network (BN) based approach to analyze risk factors influencing PSC inspections, and predict the probability of vessel detention. Data used are related to bulker carriers of seven major countries in Europe at the ‘Pre-NIR’ time, from 2005 to 2008 in Paris MoU. Yang et al. (2018b) presented a Bayesian Network model to determine vessel detention rates, emphasizing on company performance as a risk factor. The network was developed based on inspection data of bulk carriers in 2015–2017 involving nine major countries from the Paris MoU. The remainder of the work is organized as follows. Section 2 describes the methodology used. Section 3 addresses the Bayesian Network models developed and the results obtained are presented in Section 4. Finally, Seection 5 presents the conclusions and suggestions of improvements in future works.