Distractions and motor vehicle accidents: Data mining application on fatality analysis reporting system (FARS) data files
Abstract
Purpose
This research applies data mining techniques to discover the relationship between driver inattention and motor vehicle accidents.
Design/methodology/approach
The data used in this research is obtained from the Fatality Analysis Reporting System of the National Highway Traffic Safety Administration, focused on the Maryland and Washington, DC area from years 2000 to 2003. The data are first clustered using the Kohonen networks. Then, the patterns and rules of the data are explored by decision tree and neural network models.
Findings
Results suggests that when inattention and physical/mental conditions take place at the same time, the driver has a higher tendency of being involved in a crash that collides into static objects. Furthermore, with regards to the manner of collision, the relative importance of colliding into a moving vehicle as the first harmful event is two times higher relative to that of colliding into a fixed object as the first harmful event in a crash.
Research limitations/implications
The data used in this research are limited to fatal crashes that happened in Maryland and Washington, DC from years 2000 to 2003.
Originality/value
This is one of the first research papers utilizing data mining techniques to explore the possible relationships between driver inattention and motor vehicle crashes.
Keywords
Citation
Tseng, W., Nguyen, H., Liebowitz, J. and Agresti, W. (2005), "Distractions and motor vehicle accidents: Data mining application on fatality analysis reporting system (FARS) data files", Industrial Management & Data Systems, Vol. 105 No. 9, pp. 1188-1205. https://doi.org/10.1108/02635570510633257
Publisher
:Emerald Group Publishing Limited
Copyright © 2005, Emerald Group Publishing Limited