Bayesian network model of maritime safety management |
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| Affiliation: | 1. Aalto University, School of Engineering, Department of Applied Mechanics, Research Group on Maritime Risk and Safety, Kotka Maritime Research Centre, Heikinkatu 7, FI-48100 Kotka, Finland;2. Aalto University, School of Engineering, Department of Applied Mechanics, Research Group on Maritime Risk and Safety, P.O. Box 12200, FI-00076 Aalto, Finland;1. Electrical and Computer Engineering Department, University of Miami, Coral Gables, FL 33146, United States;2. Evelyn F. McKnight Brain Institute, University of Miami, Miller School of Medicine, Miami, FL 33136, United States;1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China;2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China;3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong;1. Gradiant Research Centre, Vigo, Spain;2. AtlantTIC Research Center for Information and Communication Technologies, Department of Telematics Engineering, University of Vigo, Spain;1. College of Computer Science, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China;2. Provident Technology Pte. Ltd., 7030 Ang Mo Kio Ave 5, #03-25 Northstar, Singapore 569880, Singapore;3. State Key Laboratory of Software Engineering, Computer School, Wuhan University, 299 Bayi Road, Wuhan 430072, China |
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| Abstract: | This paper presents a model of maritime safety management and its subareas. Furthermore, the paper links the safety management to the maritime traffic safety indicated by accident involvement, incidents reported by Vessel Traffic Service and the results from Port State Control inspections. Bayesian belief networks are applied as the modeling technique and the model parameters are based on expert elicitation and learning from historical data. The results from this new application domain of a Bayesian network based expert system suggest that, although several its subareas are functioning properly, the current status of the safety management on vessels navigating in the Finnish waters has room for improvement; the probability of zero poor safety management subareas is only 0.13. Furthermore, according to the model a good IT system for the safety management is the strongest safety-management related signal of an adequate overall safety management level. If no deficiencies have been discovered during a Port State Control inspection, the adequacy of the safety management is almost twice as probable as without knowledge on the inspection history. The resulted model could be applied to performing several safety management related queries and it thus provides support for maritime safety related decision making. |
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| Keywords: | Safety management The ISM Code Bayesian networks Safety indicators Maritime traffic safety Expert elicitation |
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