Collision accident accounts for the largest proportion among all types of maritime accidents, emergency decision-making is essential to reduce the consequence of such accidents. This paper proposes a novel Bayesian Network based emergency decision-making model for consequence reduction of individual ship-ship collision in the Yangtze River. The kernel of this method is to propose a three-layer decision-making framework, to develop the graphical structure for describing the accident process and to establish the conditional probability tables for the quantitative relationships. The merits of the proposed method include the intuitive representation of accident development, easy to implement, ability to deal with incomplete information and updated information. This proposed method is applied to a typical collision accident in the Yangtze River. Consequently, this paper provides a practical and novel decision-making method for collision accidents.

Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection.

Traffic flow theory has been applied to the study of marine traffic flow and underpins the phenomena being studied. This paper is intended to demonstrate the uses of classical fundamental diagrams, which are developed previously on the basis of highway traffic, by conducting empirical tests on marine traffic data. Marine traffic data are extracted from the Automatic Identification System (AIS) from Shanghai port in 2010 and 2018. Interpolation methods are used to estimate the three primary variables of fundamental diagrams, namely, flow rate, traffic density, and local speed. The relationships among these three variables are discussed and marine traffic data are further compared and contrasted with classical traffic flow models. The importance of the finding is to create consistency, predictability and uniformity of uses of fundamental diagrams on marine traffic flow.