Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods

Dursun Delen, Leman Tomak, Kazim Topuz, Enes Eryarsoy

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Investigation of the risk factors that contribute to the injury severity in motor vehicle crashes has proved to be a thought-provoking and challenging problem. The results of such investigation can help better understand and potentially mitigate the severe injury risks involved in automobile crashes and thereby advance the well-being of people involved in these traffic accidents. Many factors were found to have an impact on the severity of injury sustained by occupants in the event of an automobile accident. In this analytics study we used a large and feature-rich crash dataset along with a number of predictive analytics algorithms to model the complex relationships between varying levels of injury severity and the crash related risk factors. Applying a systematic series of information fusion-based sensitivity analysis on the trained predictive models we identified the relative importance of the crash related risk factors. The results provided invaluable insights for the use of predictive analytics in this domain and exposed the relative importance of crash related risk factors with the changing levels of injury severity.

Original languageEnglish
Pages (from-to)118-131
Number of pages14
JournalJournal of Transport and Health
Volume4
DOIs
StatePublished - 1 Mar 2017

Keywords

  • Automobile crashes
  • Injury severity
  • Machine learning
  • Predictive analytics
  • Risk factors
  • Sensitivity analysis

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