Vehicle classification model for loop detectors using neural networks

Vehicle class is an important parameter in the process of road traffic measurement. Inductive loop detectors (ILD) and image sensors are rarely used for vehicle classification because of their low accuracy. To improve their accuracy, a new algorithm is suggested for ILD using backpropagation neural networks. In the developed algorithm, inputs to the neural networks are the variation rate of frequency and occupancy time. The output is five classified vehicles. The developed algorithm was assessed at test sites, and the recognition rate was 91.7%. Results verified that, compared with the conventional method based on ILD, the proposed algorithm improves the vehicle classification accuracy.