  Driving behaviour is one of the primary causes of road crashes and accidents,
and these can be decreased by identifying and minimizing aggressive driving
behaviour. This study identifies the timesteps when a driver in different
circumstances (rush, mental conflicts, reprisal) begins to drive aggressively.
An observer (real or virtual) is needed to examine driving behaviour to
discover aggressive driving occasions; we overcome this problem by using a
smartphone's GPS sensor to detect locations and classify drivers' driving
behaviour every three minutes. To detect timeseries patterns in our dataset, we
employ RNN (GRU, LSTM) algorithms to identify patterns during the driving
course. The algorithm is independent of road, vehicle, position, or driver
characteristics. We conclude that three minutes (or more) of driving (120
seconds of GPS data) is sufficient to identify driver behaviour. The results
show high accuracy and a high F1 score.

Machine Learning;Artificial Intelligence