Comparative Analysis of Traffic Congestion Prediction Models for Cellular Mobile Macrocells

Aliyu Ozovehe, Okpo U. Okereke, Anene E. Chibuzo, Abraham U. Usman


Traffic congestion prediction is a non-linear process that involves obtaining valuable information from a set of traffic data and regression or auto-regression linear models cannot be applied as they are limited in their ability to deal with such problems. However, Artificial Intelligent (AI) techniques have shown great ability to deal with non-linear problems and two of such techniques which have found application in traffic prediction are the Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). In this work, Multiple Layer Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Network (RBF-NN), Group Method of Data Handling (GMDH) and an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are trained based on busy hour (BH) traffic measurement data taken from some GSM/GPRS sites in Abuja, Nigeria. The trained networks were then used to predict traffic congestion for some macrocells and their accuracy are compared using four statistical indices. The GMDH model on the average gave goodness of fit (R2), root mean square error (RMSE), standard deviation (σ), and mean absolute error (µ) values of 99, 3.16, 3.53 and 2.32 % respectively. It was observed that GMDH model has the best fit in all cases and on the average predict better than ANFIS, MLP and RBF models. The GMDH model is found to offer improved prediction results in terms of increasing the R2 by 20% and reducing RMSE by 60% over ANFIS, the closest model to the GMDH in term of prediction accuracy.


Artificial Intelligent Network; Quality of Service; Busy Hour Traffic and Traffic Congestion

Full Text:



O. Aliyu, U. O Okpo, E. C. Anene and U. U. Abraham, “Busy hour traffic congestion analysis in mobile macrocells,” Nigerian Journal of Technology (NIJOTECH) Vol. 36, No. 4, pp. 1265–1270, October 2017.

S. Ardhan, S. Satsri, V. Chutchavong and O. Sangaroon, “Improved model for traffic fluctuation prediction by Neural network,” International Conference on Control, Automation and Systems, Seoul, Korea, Oct. 17-20,2007.

F. Z. Mohamed and E. Halima, “Analysis and prediction of real network traffic, Journal of Networks,” Vol. 4, No. 9, November 2009.

S. Garba, B.G. Bajoga, M.B. Mu’azu, D.D. Dajab, and U.F Abdu-Aguye, Development of an ANFIS-based QoS model for a GSM service provider (MTN Nigeria Kano region network), 3rd IEEE International Conference on Adaptive Science and Technology (ICAST 2011).

R. E. Sivakumar, K Ashok, and G. Sivaradje, “Prediction of traffic load in wireless network using time series model,” Department of Electronics and Communication Engineering Pondicherry Engineering College, Puducherry-605 014, India, 978-1-61284-764-1/11/2011.

P. Gaurav, M. S Khadim, and A.K. Choudhary, “Telecom voice traffic prediction for gsm using feed forward neural network,” International Journal of Engineering Science and Technology (IJEST), ISSN: 0975-5462 Vol. 5 No.03, March 2013.

E.O Oladeji, E.N Onwuka, M. A. Aibinu, “Determination of voice traffic busy hour and traffic forecasting in global system of mobile communication (GSM) in Nigeria, 11th Malaysia International Conference on Communications, 26th-28th November 2013.

P. Aurabind, R. Anubhav, M. Vaibhav, and G. Anshul, “Development of a smart grid prototype for the proposed 33kv distribution system,” Measurement of Electrical Engineering, National Institute of Technology Rourkela, 2011.

A. Simaneka, “Development of models for short-term load forecasting using Artificial Neural Network,” Master’s Thesis, Faculty of Engineering, Cape Peninsula University of Technology, November 2008.

E. Ostlin, H. J Zepernick, and H. Suzuki, “Macrocell radio wave propagation prediction using an artificial neural network,” IEEE Semiannual Vehicular Technology Conference, 1, 57–61, 2004.

B. Koo, S. Lee, W. Kim, and J. H. Park, “Comparative study of short-term electric load forecasting,” Fifth International Conference on Intelligent Systems,” Modeling and Simulation, 2014.

K. Atashkari, N., Nariman-Zadeh, M. Gölcü, A. Khalkhali, and A. Jamali, “Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms,” Energy Conversion and Management Journal, 48(3) 1029-1041, 2007.

S Edmund, Yu. Roger and C.Y. Chen, “Traffic prediction using neural networks, school of computer and information sciences,” Syracuse University, Syracuse, New York, 0-7803-0917-0, 1993.

T. Edwards, D.S.W. Tansley, R. J. Frank, N. Davey, “Traffic trends analysis using neural networks,” Faculty of Information Sciences University of Hertfordshire Hatfield, Herts., UK, AL10 9AB, 1998.

H. Jason and M. Philip, The limitations of artificial neural networks for traffic prediction, School of Engineering, University of Durham, Durham, UK {J.L.Hall, Philip.Mars}, 1998.

E. D. Markus, O. U. Okereke and J. T. Agee, “Predicting Telephone Traffic Congestion using Multi-Layer Feedforward Neural Networks,” Advanced Materials Research Vol. 367 pp 191-198, 2012.

M.A Raheem, and O.U. Okereke, “A Neural Network Approach to GSM Traffic Congestion Prediction”, American Journal of Engineering Research (AJER) e-ISSN: 2320-0847 p-ISSN: 2320-0936 Volume-03, Issue-11, pp-131-138, 2014.

A. Ozovehe, “Group Method of Data Handling for Modeling Macrocell Traffic Congestion, “Zaria Journal of Electrical Engineering Technology, Department of Electrical and Computer Engineering, Ahmadu Bello University, Zaria, Nigeria. Vol. 4 No. 1 and 2, March and September, ISSN: 0261-1570, pp. 46 – 52, 2015.

M. Negnevitsky, Artificial Intelligence, Pearson Education Limited, 2005.

MATLAB, “Matlab software,” Neural Network Toolbox™, 1984-2013.



  • There are currently no refbacks.

Copyright (c) 2018 Aliyu Ozovehe