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Aircraft safety analysis using clustering algorithms

Author

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  • Olja Čokorilo
  • Mario De Luca
  • Gianluca Dell’Acqua

Abstract

In recent years, there have been many cost-benefit studies on aviation safety, which deal mainly with economic issues, omitting some strictly technical aspects. This study compares aircraft accidents in relation to the characteristics of the aircraft, environmental conditions, route, and traffic type. The study was conducted using a database of over 1500 aircraft accidents worldwide, occurring between 1985 and 2010. The data were processed and then aggregated into groups, using cluster analysis based on an algorithm of partition binary ‘Hard c means.’ For each cluster, the ‘cluster representative’ accident was identified as the average of all the different characteristics of the accident. Moreover, a ‘hazard index’ was defined for each cluster (according to annual movements); using this index, it was possible to establish the dangerousness of each ‘cluster’ in terms of aviation accidents. Obtained results allowed the construction of an easy-to-use predictive model for accidents using multivariate analysis.

Suggested Citation

  • Olja Čokorilo & Mario De Luca & Gianluca Dell’Acqua, 2014. "Aircraft safety analysis using clustering algorithms," Journal of Risk Research, Taylor & Francis Journals, vol. 17(10), pages 1325-1340, November.
    • Handle: RePEc:taf:jriskr:v:17:y:2014:i:10:p:1325-1340
    DOI: 10.1080/13669877.2013.879493
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    Cited by:

    1. Omrani, Farzane & Etemadfard, Hossein & Shad, Rouzbeh, 2024. "Assessment of aviation accident datasets in severity prediction through machine learning," Journal of Air Transport Management, Elsevier, vol. 115(C).
    2. Jahangoshai Rezaee, Mustafa & Yousefi, Samuel, 2018. "An intelligent decision making approach for identifying and analyzing airport risks," Journal of Air Transport Management, Elsevier, vol. 68(C), pages 14-27.
    3. Javier Cano & Alessandro Pollini & Lorenzo Falciani & Uğur Turhan, 2016. "Modeling current and emerging threats in the airport domain through adversarial risk analysis," Journal of Risk Research, Taylor & Francis Journals, vol. 19(7), pages 894-912, August.
    4. Yaser Yousefi & Nader Karballaeezadeh & Dariush Moazami & Amirhossein Sanaei Zahed & Danial Mohammadzadeh S. & Amir Mosavi, 2020. "Improving Aviation Safety through Modeling Accident Risk Assessment of Runway," IJERPH, MDPI, vol. 17(17), pages 1-36, August.
    5. Boštjan Kovačič & Damjan Doler & Drago Sever, 2021. "The Innovative Model of Runway Sustainable Management on Smaller Regional Airports," Sustainability, MDPI, vol. 13(2), pages 1-20, January.
    6. Gore, Ninad & Arkatkar, Shriniwas & Joshi, Gaurang & Antoniou, Constantinos, 2023. "Developing modified congestion index and congestion-based level of service," Transport Policy, Elsevier, vol. 131(C), pages 97-119.

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