Vehicle classification using road side sensors and feature-free data smashing approach

The main contribution of this paper is a study of the applicability of data smashing - a recently proposed data mining method - for vehicle classification according to the “Nordic system for intelligent classification of vehicles” standard, using measurements of road surface vibrations and magnetic field disturbances caused by passing vehicles. The main advantage of the studied classification approach is that it, in contrast to the most of traditional machine learning algorithms, does not require the extraction of features from raw signals. The proposed classification approach was evaluated on a large dataset consisting of signals from 3074 vehicles. Hence, a good estimate of the actual classification rate was obtained. The performance was compared to the previously reported results on the same problem for logistic regression. Our results show the potential trade-off between classification accuracy and classification method's development efforts could be achieved.