An accident is defined as an unfortunate incident that happens unexpectedly and unintentionally, typically resulting in damage or injury. Considering all the consequences that could eventuate after an accident, there are reasons to believe that a normal person does not drive with an ex-ante intention to cause an accident. Holding a valid driving license is prerequisite to drive in any part of the world and during the licensing

Driving behaviour is a critical issue in modern transportation systems due to the increasing concerns about the safety of drivers, passengers, and road users. Machine learning models are capable of learning driving patterns from sensor data and recognizing individuals by their driving behaviours. This paper presents a novel framework for aggressive driving detection and driver classification based on driving events identified from GPS data collected with smartphones and heart rate of the driver captured with a wearable device. The proposed system for road rage and aggressive driving detection (RAD) is realized with an integral framework with components for data acquisition, event detection, driver classification, and model interpretability. The system is implemented by generating a prediction model by training machine learning classifiers with a dataset collected in a cohort to classify drivers into good, unhealthy, road rage, and always bad. The proposed system is to improve road safety and to customize insurance premiums in the best interest of policy holders and insurance companies.