Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods. (March 2017)

• Record Type: Journal Article
• Authors: Delen, Dursun; Tomak, Leman; Topuz, Kazim; Eryarsoy, Enes
• Is Part Of: Journal of transport & health. Volume 4(2017:Mar.)
• Page Start: 118
• Page End: 131
• Publication Date: 2017-03
• Subjects: Automobile crashes -- Predictive analytics -- Risk factors -- Injury severity -- Machine learning -- Sensitivity analysis; Transportation -- Health aspects -- Periodicals; Transportation -- Periodicals; Public Health -- Periodicals; Noise, Transportation -- Periodicals; Air Pollutants -- Periodicals; 388
• DOI: 10.1016/j.jth.2017.01.009
• Languages: English
• ISSNs: 2214-1405

Abstract

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. Highlights: Predictive analytics methods are capable of analyzing highly complex problems. Analysis of injury severity risk factors is an interesting and challenging problem. This study developed several analytic models using a large and feature rich data set. SVM generated the best prediction results on a 10-fold cross validation dataset. Sensitivity analysis is used to identify the relative importance of injury related risk factors.