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Driving data record information on style and patterns of vehicles that are in motion. These data are analysed to obtain risk scores that can later be implemented in insurance pricing schemes. Scores may also be used in onboard sensors to create risk alerts that help drivers to keep up with safety margins. Regression methods are proposed and a prototype real sample of 253 drivers is analysed. Conclusions are drawn on the mean number of brake pulses per day as measured within 30 seconds time-intervals. Linear and logistic regressions serve to construct a label that classifies drivers. A novel factor based on the driving range that is defined from geo-localization improves the results considerably. Driving range is expressed as measures the diagonal of a rectangle that contains the furthest North-South versus East-West weekly vehicle trajectory. This factor shows that frequent braking activity is negatively related to the square of driving range.

Regression scores to identify risky drivers from braking pulses