The Arctic remains one of the most inhospitable places on earth for both man and machine. However, for hundreds of years, sea routes have been sought to reduce the transit time of vessels and therefore achieve an economic advantage. Navigation of these routes pose a unique set of challenges including the presence of ice, extended hours of darkness and inaccessibility for emergency response.
To meet these challenges, my research focuses on applying intelligent big data analytics to promote safe and efficient navigation in the Arctic. Through the application of machine learning and risk analysis, optimised vessel routes will be developed which maximise the safety of navigation whilst minimising fuel consumption. Pre-processing and fusion of a multitude of datasets will be undertaking including data form the Automatic Identification System, satellite ice observations, historical incidents and hydrographic surveys through the use of big data geospatial processing algorithms.
The output of this research will both allow for the optimal planning of Arctic navigation and improve our understanding of how future Arctic shipping routes may change due to the effects of climate change. This research will be conducted in conjunction with the Horizon 2020 SEDNA project.
My supervision team is spread across both Electronics and Computer Science and the Business school and I am guided by Dr Zoheir Sabeur (IT Innovation Centre), Dr Long Tran-Thahn (Agents, Interactions and Complexity) and Dr Mario Brito (Centre for Risk Research).
Rawson, Andrew, David
An analysis of vessel traffic flow before and after the grounding of the MV Rena, 2011.
12th International Conference on Marine Navigation and Safety of Sea Transportation, Poland.
21 - 23 Jun 2017.
Rawson, Andrew David and Rogers, Edward
Assessing the impacts to vessel traffic from offshore wind farms in the Thames Estuary.
Scientific Journals of the Maritime University of Szczecin, 43 (115), .
Rawson, Andrew David, Rogers, Ed, Foster, David and Phillips, David
Practical application of domain analysis: port of London case study.
Journal of Navigation, 67 (2), .
Rawson, Andrew David and Riding, John
Improving movement management using GIS technology: modern tools for the harbour master.
International Harbour Masters Congress, Belgium.
26 - 30 May 2014.
Rawson, Andrew, David, Rogers, Edward and Towens, Mark
Determination of vessel traffic capacity in Central London.
International Harbour Masters Congress, Canada.
30 May - 03 Jun 2016.
Rawson, Andrew, David, Sabeur, Zoheir and Correndo, Gianluca
Spatial challenges of maritime risk analysis using big data.
Soares, C. Guedes
In Proceedings of the 8th International Conference on Collision and Grounding of Ships and Offshore Structures (ICCGS 2019).
CRC Press / Balkema.
Rawson, Andrew David and Brito, Mario
Modelling of ship navigation in extreme weather events using machine learning.
In Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment And Management Conference.