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.
| Published in |
Science Journal of Circuits, Systems and Signal Processing (Volume 3, Issue 6-1)
This article belongs to the Special Issue Computational Intelligence in Digital Image Processing |
| DOI | 10.11648/j.cssp.s.2014030601.12 |
| Page(s) | 6-16 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Transportation System, Traffic Light Controller System, Associative Memory, MCA Associative Memory
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APA Style
Emad I. Abdul Kareem, Safana H. Abbas, Salman Mahmood Salman. (2014). Intelligent Traffic Light Controller Based on MCA Associative Memory. Science Journal of Circuits, Systems and Signal Processing, 3(6-1), 6-16. https://doi.org/10.11648/j.cssp.s.2014030601.12
ACS Style
Emad I. Abdul Kareem; Safana H. Abbas; Salman Mahmood Salman. Intelligent Traffic Light Controller Based on MCA Associative Memory. Sci. J. Circuits Syst. Signal Process. 2014, 3(6-1), 6-16. doi: 10.11648/j.cssp.s.2014030601.12
AMA Style
Emad I. Abdul Kareem, Safana H. Abbas, Salman Mahmood Salman. Intelligent Traffic Light Controller Based on MCA Associative Memory. Sci J Circuits Syst Signal Process. 2014;3(6-1):6-16. doi: 10.11648/j.cssp.s.2014030601.12
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Download@article{10.11648/j.cssp.s.2014030601.12,
author = {Emad I. Abdul Kareem and Safana H. Abbas and Salman Mahmood Salman},
title = {Intelligent Traffic Light Controller Based on MCA Associative Memory},
journal = {Science Journal of Circuits, Systems and Signal Processing},
volume = {3},
number = {6-1},
pages = {6-16},
doi = {10.11648/j.cssp.s.2014030601.12},
url = {https://doi.org/10.11648/j.cssp.s.2014030601.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.s.2014030601.12},
abstract = {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.},
year = {2014}
}
TY - JOUR T1 - Intelligent Traffic Light Controller Based on MCA Associative Memory AU - Emad I. Abdul Kareem AU - Safana H. Abbas AU - Salman Mahmood Salman Y1 - 2014/11/06 PY - 2014 N1 - https://doi.org/10.11648/j.cssp.s.2014030601.12 DO - 10.11648/j.cssp.s.2014030601.12 T2 - Science Journal of Circuits, Systems and Signal Processing JF - Science Journal of Circuits, Systems and Signal Processing JO - Science Journal of Circuits, Systems and Signal Processing SP - 6 EP - 16 PB - Science Publishing Group SN - 2326-9073 UR - https://doi.org/10.11648/j.cssp.s.2014030601.12 AB - 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. VL - 3 IS - 6-1 ER -
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