Predicting Telephone Traffic Congestion Using Multi Layer Feedforward Neural Networks

Abstract:

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.

• Periodical: Advanced Materials Research (Volume 367)
• Pages: 191-198
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.367.191
• Online since: October 2011
• Authors: E.D. Markus, O.U. Okereke, John T. Agee
• Keywords: Backpropagation, Congestion, Feed Forward NN, Network Traffic, Prediction

References

[1] ITU – Recommendation, 1. 371 (previously CCITT Recommendation), Traffic Control and Congestion Control in B-ISDN. (1993).

[2] Andreas P. and Ahmet S., Congestion Control: Book CI Congestion-Control- final. (Tech. doc. ). University of Cyrus, Nicosia Cyrus (1999).

[3] Chen, B. S., S. C. Peng, et al. Traffic modeling, prediction, and congestion control for high-speed networks: A fuzzy AR approach. IEEE Transactions on Fuzzy Systems, 8(5): (2000) 491-508.

DOI: 10.1109/91.873574

[4] Ishak, S. and H. Al-Deek, Evaluation of short-term time-series traffic prediction model. Journal of transportation engineering 128(6): (2002). 490-498.

DOI: 10.1061/(asce)0733-947x(2002)128:6(490)

[5] Zhou C. and Nelson P. C., Predicting Traffic Congestion using Recurrent Neural Networks, Proceedings of the 9th World Congress on Intelligent Transport Systems, Chicago, USA, (2002).

[6] Oki, E. and A. Iwaki. Performance comparisons of optimal routing by pipe, hose, and intermediate models. Sarnoff Symposium, 2009. SARNOFF '09, (2009), IEEE.

DOI: 10.1109/sarnof.2009.4850317

[7] Dudul S. V. and Ghatol, Identification of Linear Dynamical Time- Invariant Systems using Feedforward Neural Network. IE(I) Journal vol 84, (2004) pp.66-71.

[8] Monica Adya and Fred Callopy, How effective are Neural Networks at Forecasting Prediction? A Review and Evaluation, Journal of forecasting, University of Maryland Baltimore Counti USA, J. forecast 17, (1998) pp.481-495.

DOI: 10.1002/(sici)1099-131x(1998090)17:5/6<481::aid-for709>3.0.co;2-q

[9] Chichoki, A. and Unbehauen, R., Neural Networks for Optimization and Signal Processings. John Wiley and Sons Inc. USA, (1993).

[10] Goodman R. M. and Ambrose B. E. Neural Networks Applied to Traffic Management in Telephone Networks. Proceedings of the IEEE. Vol 84, no 10, (1992) 1421-1429.

DOI: 10.1109/5.537108

[11] Zhang, G. P. and M. Qi, Neural network forecasting for seasonal and trend time series, European Journal of operational research 160(2) (2005) 501-514.

DOI: 10.1016/j.ejor.2003.08.037

[12] Lin, X., X Wang, et al. Experiment on neural network prediction modeling of far infrared radiation drying for agaricus bisporus. Trans of the Chinese Soc of Agric Machinery 41(5): (2010) 110-114.