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

Delen D., Tomak L., Topuz K., Eryarsoy E.

JOURNAL OF TRANSPORT & HEALTH, vol.4, pp.118-131, 2017 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 4
  • Publication Date: 2017
  • Doi Number: 10.1016/j.jth.2017.01.009
  • Journal Name: JOURNAL OF TRANSPORT & HEALTH
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.118-131
  • Keywords: Automobile crashes, Predictive analytics, Risk factors, Injury severity, Machine learning, Sensitivity analysis, VECTOR MACHINE MODELS, METHODOLOGICAL ALTERNATIVES, STATISTICAL-ANALYSIS, TRAFFIC ACCIDENTS, 2-VEHICLE CRASHES, DRIVERS, NETWORKS, SAFETY
  • Ondokuz Mayıs University Affiliated: Yes

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. (C) 2017 Elsevier Ltd. All rights reserved.