Recognizing vehicle classification information from blade sensor signature

Traffic surveillance system capable of providing accurate real-time traffic measurements is a backbone of fully exploiting a variety of advanced traffic management systems. Vehicle classification information is one of the important measurements that we need to obtain in practice, which is invaluable for various aspects of transportation including engineering and planning. This study develops vehicle classification algorithms using inductive signatures obtained from a prototype innovative loop sensor, known as a 'blade'. A probabilistic neural network (PNN), a neural network implementation of multivariate Bayesian classification scheme, and a heuristic classification algorithm are employed to classify vehicle types. Vehicle feature vectors representing the vehicle shapes are extracted from blade signatures, and then utilized as inputs of the proposed algorithm. The classification performances are investigated with four different types of vehicles including passenger car, pick-up truck, sports utility vehicle, and van. N-fold cross validation is applied to evaluate the performances. Encouraging result of 70.8% overall correct classification rate obtained from the PNN-based classification algorithm demonstrates the technical feasibility of the proposed algorithm for obtaining vehicle classification information.