accidents: accidents Bayesian Network

In bnRep: A Repository of Bayesian Networks from the Academic Literature

accidents Bayesian Network

Description

Analysis of maritime transport accidents using Bayesian networks.

Format

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:

AccidentType

(Collision, Grounding, Flooding, Fire/Explosion, Capsize, Contact/Crush, Sinking, Overboard, Others);

EquipmentDevice

(Devices and equipment on board operate correctly, Devices and equipment not fully utilised or operated correctly);

ErgonomicDesign

(Ergonomic friendly, Ergonomic impact of innovative bridge design);

FairwayTraffic

(Good, Poor);

GrossTonnage

(Less than 300, 300-1000, More than 1000, NA);

HullType

(Steel, Wood, Aluminium, Others);

Information

(Effective and updated information provided, Insufficient or lack of updated information);

Length

(Less than 100, More than 100, NA);

SeaCondition

(Good, Poor);

ShipAge

(0,5, 6-10, 11-15, 16-20, More than 20, NA);

ShipOperation

(Towing, Loading/Unloading, Pilotage, Manoeuvring, Fishing, At anchor, On passage, Others);

ShipSpeed

(Normal, Fast);

ShipType

(Passenger vessel, Tug, Barge, Fishing vessel, Container ship, Bulk carrier, RORO, Tanker or chemical ship, Cargo ship, Others);

TimeOfDay

(7am to 7pm, Other);

VesselCondition

(Good, Poor);

VoyageSegment

(In port, Departure, Arrival, Mid-water, Transit, Others);

WeatherCondition

(Good, Poor);

Value

An object of class bn.fit. Refer to the documentation of bnlearn for details.

References

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.

bnRep documentation built on April 12, 2025, 1:13 a.m.