An NTUA Diploma Thesis called “Identification of driver’s risky behavior level and duration with machine learning techniques” was recently presented by Theodoros Garefalakis. For the purpose of this Diploma Thesis, high resolution data related to driving behavior were collected through a driving simulator experiment. In the first part of the analysis, four machine learning algorithms were developed to classify driver behavior into one of three risk levels, with the ‘Random Forests’ algorithm scoring the highest performance. In the context of investigating the influence of driving factors to identify driving behavior, the distance traveled, speed and speed limit emerged as the most important. In the second part of the analysis, the effect of driving characteristics on driving duration at different stages was examined through three regression algorithms. The results showed that the maximum speed was the most important variable, which negatively affects the driving duration at each safety level.