Circuit-switched traffic forecasting using neural networks

This paper describes the application of neural networks to near real-time traffic forecasting. Backpropagation networks have been trained using two years of real data, and various variables have been used as input to show the interest of taking into account the day of prediction and time of the day. Results obtained using the neural approach compare favourably to those of traditional methods.