Multivariate Techniques In Analysis Of Crash Event Involving Drivers Of Personal Vehicle in Vienna, Austria

Drivers participating in urban traffic with their personal vehicle are the most protected ones when compared to other road participants. Seatbelt and airbag along with carrosserie of vehicle makes them less exposed to injury severity. According to original police data regarding those crashes in Vienna there is a slight decrease in total number of recorded crashes along observed period. Still high in size of the number of crash events, presents worrying problem that hasn't enjoyed necessary attention. In response to this knowledge gap, secondary analysis is applied on original data using sophisticated multivariate techniques; factorial and regression analytical methods to define important indicators, factors and predictors that are significantly influencing increase of the size of the number of crash event involving this group of drivers. Cluster analysis pointed ‘hit and run’ drivers as the most dangerous ones. Six factors explaining total variance were extracted and used in regression analysis to define predictors that are undoubtedly influencing the size of the number of crash events. Discriminant model explains more than one fourth, 16% respectively, of hit & stay/go drivers. Findings reached with this research suggest the need for ‘probational extension’ of driving license for older drivers due to felony behavior increase.