An analysis of wintertime navigational accidents in the Northern Baltic Sea

• Published in: Safety science Vol. 92; pp. 66 - 84
• Main Authors: Goerlandt, Floris, Goite, Habtamnesh, Valdez Banda, Osiris A., Höglund, Anders, Ahonen-Rainio, Paula, Lensu, Mikko
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.02.2017; Elsevier BV
• Subjects: Accident conditions; Accident data analysis; Accidents; AIS data; Atmospherics; Automatic identification; Automation; Data analysis; Data mining; Data processing; Data sources; Dynamical systems; Integration; Maritime risk; Maritime safety; Navigation behavior; Occupational safety; Oil spills; Qualitative analysis; Risk analysis; Risk management; Sea ice; Ship accidents & safety; Shipping; Visual data mining; Visual perception; Winter navigation operations; ANE, Baltic Sea; DWT; Winter navigation operations; FMI; HFACS; Visual data mining; SoE; IMO; AIS; GT; FSWNS; HIRLAM; Maritime risk; GISIS; MMSI; ECMWF; MESAN; AIS data; Accident data analysis; Maritime safety; HIROMB; PCP; SMHI
• ISSN: 0925-7535; 1879-1042
• DOI: 10.1016/j.ssci.2016.09.011

•Various data sources are integrated to construct a winter navigation accident database.•AIS videos are made for investigating impact conditions in collisions and groundings.•Database is analysed using multi-attribute visual data mining techniques.•Several patterns are identified concerning conditions under which accidents occur.•Strength of evidence is assessed to gain insight in uncertainty of identified patterns. Navigational accidents in wintertime conditions occur relatively frequently, yet there is little systematic knowledge available about the circumstances under which these occur. This paper presents an analysis of navigational shipping accidents in the Northern Baltic Sea area which occurred in the period 2007–2013. The analysis is based on an integration of various data sources, aiming to reconstruct the accident conditions based on the best available data sources. Apart from basic accident information from the original accident databases, data from the Automatic Identification System is used to obtain insight in the operation type during which the accident occurred, as well as into other dynamic aspects of the accident scenario. Finally, atmospheric and sea ice data is used to reconstruct the navigational conditions under which the accidents occurred. The analysis aims to provide qualitative insights in patterns and outlier cases in the accidental conditions. Correspondingly, visual data mining is selected as analysis approach, because of its utility in obtaining qualitative knowledge from data sources through a combination of visualization techniques and human interaction with the data. Special attention is given to the strength of evidence of the identified accident patterns. The results are primarily useful for improving risk analyses focusing on oil spill risks in winter conditions and for developing realistic training scenarios for oil spill response operations.