This Diploma Thesis aims to investigate the impact of mobile phone use on driving behaviour through statistical analysis of imbalanced data using Machine Learning techniques. For classification and regression of mobile phone usage, telematics data from the OSeven telematics company, collected from naturalistic measurements, were used. Mobile phone use was defined as an indicator of risky behaviour and classification was performed on two levels of driving behaviour (risky and not risky). In the first part of the analyses, a total of four classification algorithms were developed, two including all the independent variables under consideration and the same two algorithms with the five most significant of them, as derived from the Feature Importance method. Variables related to travel speed were found to be the most significant independent variables, while according to the classification evaluation metrics, the most appropriate model was considered to be that of ‘Linear Discriminant Analysis’. In the second part of the analyses, an identical procedure was followed for the regression with the dependent variable of mobile phone usage duration with the ‘Adaptive Strengthening’ algorithm with all independent variables showing better predictive ability and the variable of road trip duration being considered the most significant.
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