Science Journal of Circuits, Systems and Signal Processing
Volume 3, Issue 6-1, December 2014, Pages: 6-16
Received: Sep. 22, 2014;
Accepted: Oct. 31, 2014;
Published: Nov. 6, 2014
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Emad I. Abdul Kareem, Department of Computer Science/ Education Collage/ Al-Mustansiriya University, Baghdad, Iraq.
Safana H. Abbas, Department of Computer Science/ Education Collage/ Al-Mustansiriya University, Baghdad, Iraq.
Salman Mahmood Salman, Department of Computer Science/ Education Collage/ Al-Mustansiriya University, Baghdad, Iraq.
Traffic in urban areas is mainly regularized by traffic lights, which may lead to the unnecessary long waiting times for vehicles if not efficiently configured. This inefficient configuration is unfortunately still the case in a lot of urban areas where most of the traffic lights are based on a ‘fixed cycle’ protocol. This paper aims to design an intelligent controller of an intersection in a specific city using associative memory with multi-connect architecture via using this structure of neural network the intelligent controller can adapt to all street cases, which may be faced during its work. Not like other controllers, this work uses small associative memory. It will learn all street traffic conditions. The controller uses virtual data about the traffic condition of each street in the intersection. Thus, in an image processing module this video camera will provide visual information. This information will be processed to extract data about the traffic jam. This data will be represented in a look- up table, then smart decisions are taken when the intersection management determines the street case of each street at the intersection based on this look- up table.
Emad I. Abdul Kareem,
Safana H. Abbas,
Salman Mahmood Salman,
Intelligent Traffic Light Controller Based on MCA Associative Memory, Science Journal of Circuits, Systems and Signal Processing. Special Issue: Computational Intelligence in Digital Image Processing.
Vol. 3, No. 6-1,
2014, pp. 6-16.
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