Muhammad Bashir Muazu

Work place: Ahmadu Bello University/Department of Computer Engineering, Zaria, 234, Nigeria

E-mail: mbmuazu@abu.edu.ng

Website:

Research Interests: Computer systems and computational processes, Artificial Intelligence, Solid Modeling, Data Structures and Algorithms, Combinatorial Optimization

Biography

Muhammad Bashir Mu’azu is a Professor of Computational Intelligence. He obtained his B.Eng (Electrical Engineering) in 1991, MSc. (Electrical Engineering) in 2001 and Ph.D. (Electrical Engineering) with a specialization in Computational Intelligence in 2006 from Ahmadu Bello University Zaria, Nigeria. He has authored several refereed technical journal articles and conference proceedings. His research interests include; Control System Dynamics, Modeling and Optimization, Fuzzy Time Series Forecasting and Artificial Intelligence Optimization.

Author Articles
Development and Simulation of Adaptive Traffic Light Controller Using Artificial Bee Colony Algorithm

By Risikat Folashade Adebiyi Kabir Ahmad Abubilal Muhammad Bashir Muazu Busayo Hadir Adebiyi

DOI: https://doi.org/10.5815/ijisa.2018.08.06, Pub. Date: 8 Aug. 2018

This paper proposes an adaptive traffic control system that dynamically manages traffic phases and durations at cross-intersection. The developed model optimally schedules green light timing in accordance with traffic condition on each lane in order to minimize the Average Waiting Time (AWT) at the cross intersection. A MATLAB based Graphic User Interface (GUI) traffic control simulator was developed. Three scenarios of vehicular traffic control were simulated and the results presented. The results show that scenario one and two demonstrated the variation of the AWT and Performance of the developed algorithm with changes in the maximum allowable green light timing over the simulation interval. In the third scenario, an AWT of 38sec was recorded against a maximum allowable green light duration of 120sec, during which 1382 vehicles were evacuated from the intersection, leaving 22 vehicles behind. The algorithm also had a performance of 98.43% over a simulation duration of 1800sec.

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