Transportation risk analysis framework for Arctic waters

Faisal Khan, Ming Yang, Brian Veitch, Soren Ehlers, Shuhong Chai

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

18 Citations (Scopus)


Arctic waters have historically been relatively inaccessible for marine transport. Lately, climate change has made more of this region ice-free in the summer season. This has reduced the difficulty of marine transport in Arctic waters. Further, exploration and development of natural resources is increasing in Arctic regions, as is destinational shipping. The unique risk factors of this region, such as extremely low temperature, ice conditions and drifting icebergs, continue to pose threats to transportation. Potential impacts associated with marine transportation accidents warrant contingency plans that recognize that preventative measures may fail. To plan effectively, a transportation accident risk assessment model for Arctic waters is helpful. There is limited work on the development of such models. A new cause-consequences based risk assessment model is proposed here. The model estimates the probability of a transportation accident and also the related consequences during navigation in Arctic waters. To illustrate the application of the methodology, it is applied to a case of an oil-tanker collision on the Northern Sea Route.

Original languageEnglish
Title of host publicationPolar and Arctic Science and Technology
ISBN (Electronic)9780791845561
Publication statusPublished - 1 Jan 2014
Externally publishedYes
EventASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2014 - San Francisco, United States
Duration: 8 Jun 201413 Jun 2014

Publication series

NameProceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE


ConferenceASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2014
CountryUnited States
CitySan Francisco


  • Arctic transportation
  • Bayesian network
  • Risk assessment
  • Ship accidents


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