Abstract
In this paper, a methodology based on Bayesian Network (BN) was proposed to deal with the difficulty of risk analysis in RoPax transport. Based on data collection and expert survey, BN model for RoPax sailing risk analysis was constructed first. Then the Expectation Maximization (EM) algorithm for parameter learning and Evidence Prepropagation Importance Sampling (EPIS) algorithm for reasoning were designed. Finally, a sensitivity analysis was conducted. To validate the model algorithms, a case study on the RoPax system of Bohai gulf in China was provided. Results indicate that the BN model can effectively address the problem of data deficiency and mutual dependency of incidents in risk analysis. It can also model the development process of unexpected hazards and provide decision support for risk mitigation.
Highlights
RoPax ships have the roll-on and roll-off facilities for carrying private or commercial vehicles
A methodology based on Bayesian Network (BN) is proposed to deal with the difficulty of sailing risk analysis in RoPax transport
Methodology based on BN for RoPax sailing risk analysis is developed in Section 3, including network construction, parameter learning, network inference and sensitivity analysis
Summary
RoPax ships have the roll-on and roll-off facilities for carrying private or commercial vehicles. RoPax ships usually sail on short voyages, shuttled in strait and gulf. RoPax ships face serious maritime transport risk. Enhancing safety management of RoPax transport is becoming an urgent issue in maritime industry. In order to improve the safety of RoPax transport, extensive studies have been carried out over the past few decades. The relationship between ship design and accidents were studied thoroughly. The complexity of RoPax transport brings difficulties to quantitative risk analysis. A methodology based on Bayesian Network (BN) is proposed to deal with the difficulty of sailing risk analysis in RoPax transport. Methodology based on BN for RoPax sailing risk analysis is developed, including network construction, parameter learning, network inference and sensitivity analysis. Numerical experiments on RoPax transportation system of Bohai gulf in China is conducted in Section 4 and conclusions are provided in final section
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