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Analysis of maritime transport accidents using Bayesian networks.
An object of class bn.fit. Refer to the documentation of bnlearn for details.
A discrete Bayesian network to provide transport authorities and ship owners with useful insights for maritime accident prevention. Probabilities were given within the referenced paper. The vertices are:
(Collision, Grounding, Flooding, Fire/Explosion, Capsize, Contact/Crush, Sinking, Overboard, Others);
(Devices and equipment on board operate correctly, Devices and equipment not fully utilised or operated correctly);
(Ergonomic friendly, Ergonomic impact of innovative bridge design);
(Good, Poor);
(Less than 300, 300-1000, More than 1000, NA);
(Steel, Wood, Aluminium, Others);
(Effective and updated information provided, Insufficient or lack of updated information);
(Less than 100, More than 100, NA);
(Good, Poor);
(0,5, 6-10, 11-15, 16-20, More than 20, NA);
(Towing, Loading/Unloading, Pilotage, Manoeuvring, Fishing, At anchor, On passage, Others);
(Normal, Fast);
(Passenger vessel, Tug, Barge, Fishing vessel, Container ship, Bulk carrier, RORO, Tanker or chemical ship, Cargo ship, Others);
(7am to 7pm, Other);
(Good, Poor);
(In port, Departure, Arrival, Mid-water, Transit, Others);
(Good, Poor);
Fan, S., Yang, Z., Blanco-Davis, E., Zhang, J., & Yan, X. (2020). Analysis of maritime transport accidents using Bayesian networks. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 234(3), 439-454.