Predicting Telephone Traffic Congestion Using Multi Layer Feedforward Neural Networks
Predicting congestion in a telephone network has become part of an efficient network planning operation. The excellent capability of neural network (NN) to learn complex nonlinear systems makes it suitable for identifying the relationship between traffic congestion and the variables responsible for its occurrence in a time-varying traffic situation. This paper presents an artificial NN model for predicting traffic congestion in a telephone network. The design strategy uses a multilayered feedforward NN with backpropagation algorithm to model the telephone traffic situation. Matlab was used as a platform for all simulations. Regression analysis between predicted traffic congestion volumes and corresponding actual volumes gave a correlation coefficient of 87% which clearly shows the utility and effectiveness of Neural Networks in traffic prediction and control.
Prof. A.O. Akii Ibhadode
E.D. Markus et al., "Predicting Telephone Traffic Congestion Using Multi Layer Feedforward Neural Networks", Advanced Materials Research, Vol. 367, pp. 191-198, 2012