Comparison of classification trees and logistic regression to model the severity of collisions involving elderly drivers in Canada

Abstract

The number of drivers aged 65 years and older in Canada and the proportion of the population these drivers represent have been increasing for many years and will continue to do so in years to come. This increase in the number of elderly drivers could possibly lead to an increase in the numbers of fatalities, serious injuries and collisions involving drivers of this age group[1]. In order to find ways to reduce the number of collisions involving elderly drivers, and in particular the number of fatalities among the victims of collisions involving drivers aged 65 years and older, the relationship between the characteristics of these collisions and their severity was modeled using both classification trees and logistic regression. In this thesis, we explain the theory behind classification trees and logistic regression before analyzing the data. Both techniques are also compared based on the results of the analysis. In particular, we have validated the classification trees with the more rigorous logistic regression analysis. Consequently, the non-statistician can use the visually appealing trees with confidence.