IDEAS home Printed from https://ideas.repec.org/a/taf/jriskr/v17y2014i10p1325-1340.html
   My bibliography  Save this article

Aircraft safety analysis using clustering algorithms

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13669877.2013.879493
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13669877.2013.879493?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jriskr:v:17:y:2014:i:10:p:1325-1340. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RJRR20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.