This research applies data mining techniques to discover the relationship between driver inattention and motor vehicle accidents.
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.
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.
The data used in this research are limited to fatal crashes that happened in Maryland and Washington, DC from years 2000 to 2003.
This is one of the first research papers utilizing data mining techniques to explore the possible relationships between driver inattention and motor vehicle crashes.
