Realising advanced risk-based port state control inspection using data-driven Bayesian networks

In the past decades, maritime transportation not only contributes to economic prosperity, but also renders many threats to the industry, causing huge casualties and losses. As a result, various maritime safety measures have been developed, including Port State Control (PSC) inspections. In this paper, we propose a data-driven Bayesian Network (BN) based approach to analyse risk factors influencing PSC inspections, and predict the probability of vessel detention. To do so, inspection data of bulk carriers in seven major European countries from 2005 to 2008 1 in Paris MoU is collected to identify the relevant risk factors. Meanwhile, the network structure is constructed via TAN learning and subsequently validated by sensitivity analysis. The results reveal two conclusions: first, the key risk factors influencing PSC inspections include number of deficiencies, type of inspection, Recognised Organisation (RO) and vessel age. Second, the model exploits a novel way to predict the detention probabilities under different situations, which effectively help port authorities to rationalise their inspection regulations as well as allocation of the resources. Further effort will be made to conduct contrastive analysis between ‘Pre-NIR’ period and ‘Post-NIR’ period to test the impact of NIR started in 2008. © 2018 Elsevier Lt

Risk-Based Game Modelling for Port State Control Inspections

This thesis aims to develop a new way for port authorities to predict, analyse and make decisions in Port State Control (PSC) inspections. Under the New Inspection Regime (NIR), it is necessary to not only figure out the influence of new regime to the PSC system, but also provide some technical tools capable of predicting the inspection results and supporting the decision-making of port authorities when regulating the inspection policy. The study consists of analysis from multiple perspectives, both qualitative and quantitative. The risk factors influencing the inspection results and the decision-making of port authorities under NIR are identified through the practical inspection records and related literature. The Paris Memorandum of Understanding (MoU) offers the historical inspection records within the region of Europe and the North Atlantic basin, reflecting different conditions in different periods. Given the different inspection system since 2011, port authorities require a brand new perception of the new inspection regime to estimate the inspection results, and further make decisions when making their own inspection policy. To achieve the objective, an incorporation of two types of models that have proved popular and superior is applied in this study. One is the risk assessment model of Bayesian network (BN), the other is the decision-making model of game theory. The BN models in this research utilize a data-driven approach called Tree Augmented Naïve (TAN) learning to derive the structure of the models. Based on the inspection reports collected from Paris MoU, two BNs that represent the situations of Paris MoU inspection system in different periods are constructed. Company performance, the new indicator, is viewed as one of the important factors influencing the inspection results for the first time and considered in the models. The BN model after the implementation of NIR can serve as the prediction tool for estimating inspection results under dynamic situations. Additionally, a comparative analysis between two models is conducted to clarify the influence on PSC inspection system brought by NIR. When constructing the non-cooperative strategic game model between port authorities and ship owners under NIR, the BN model outcomes play a crucial role in this process, highlighting the novelty of this model. Through the analysis and calculation on the payoff matrix, a Nash equilibrium solution representing the theoretical optimal inspection rate for port authorities is obtained. To validate the feasibility and practical significance of the game model, an empirical study is conducted. The statistics are quantitative and collected from different sources, i.e. Basic vessel information from the World Shipping Encyclopaedia (WSE), casualty information from IMO and Lloyd's Register of Shipping, PSC Inspection records from Paris MoU online inspection database, and the estimated value of different cost types from Drewry Shipping Consultants Ltd. The empirical study illustrates the insights of the optimal inspection policy for port authorities (i.e. with the increase of punishment severity, the optimal inspection rates experience a decreasing trend whatever the vessel condition), as well as providing suggestions for them when formulating the optimal inspection policy under various situations. Based on the BN model and the strategic game model after the implementation of NIR, the thesis eventually proposes a decision-making framework for port authorities to prioritise and select the strategies under different situations. The six-step framework incorporates a risk assessment approach and decision-making approach to provide a novel way to rank the candidate options of port authorities in terms of their resources, which enables decision-makers to find optimal strategies to improve the performance of the PSC inspection system under dynamic business environments. In general, this thesis provides important insights for port authorities to ensure that optimal inspection actions are taken to improve safety at sea in a cost effective manner. The two technical tools (i.e. the dynamic prediction tool for inspection results & the optimal inspection strategy), and the decision-making framework proposed in this project are helpful for port authorities within the Paris MoU region when regulating their inspection policy under NIR. Meanwhile, the comparative analysis in this study further clarifies the influence of NIR on new inspection system from different angles for the first time, demonstrating the introduction and implementation of NIR is a wise and positive decision