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Driver Behaviour Profiling in VANETs: Comparison of Ensemble Machine Learning Techniques

The behaviour of drivers affect road traffic, expenditure of energy and vehicular emissions. Analysis of driver behaviour tries to identify the potential driving characteristics of an individual driver. This behaviour analysis requires lots of data which is usually collected from the sensors present in the On Board Unit(OBU). The process is usually automated and the sensors are polled for the data. The collected data is used to build machine models which are then used to create a classification scheme which is used to profile the behaviour. This paper explores data from multiple Android based smartphone sensors, and different ensemble classification based machine learning algorithms in order to investigate which technique results in the highest performance.