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), 2014. Published by Science Publishing Group |
Transportation System, Traffic Light Controller System, Associative Memory, MCA Associative Memory
| [1] | Wiering M. and Veenen J. V. and Vreeken J. and Koopman A.,Intelligent Traffic Light Control, Ercim News, European Research Consortium For Informatics And Mathematics, 2004. |
| [2] | Pérez J., P. G., Silva J. , Cabello E. , Monclús J. and Cristina T. S., A Conflict-Avoiding, Artificial Vision Based, Intelligent Traffic Light Controller. Universidad Rey Juan Carlos,ESCET,C/ Tulipán S/N,28933 Móstoles (Spain),(2) RACE,C/ Isaac Newton 4,28760 Tres Cantos (Madrid), 2004. |
| [3] | Proulx, Viera K., Jeff Raab, and Richard Rasala, Traffic light: A pedagogical exploration through a design space., Journal of Computing Sciences in Colleges. Vol. 15. No. 5. Consortium for Computing Sciences in Colleges, 2000. |
| [4] | Cucchiara R., Image Analysis and Rule-Based Reasoning for a Traffic Monitoring System, IEEE transactions on intelligent transportation systems, vol. 1, no. 2, 2000. |
| [5] | El-Medany, W. M., and M. R. Hussain, FPGA-based advanced real traffic light controller system design. Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2007. IDAACS 2007. 4th IEEE Workshop on. IEEE, 2007. |
| [6] | Silvert W., Fuzzy Logic Modelling of Traffic Light, Indicators: DFO Report, 2001. |
| [7] | Gradinescu V., Gorgorin C., Diaconescu R., Cristea V. , Iftode L., Adaptive Traffic Lights Using Car-to-Car Communication , IEEE, 2004. |
| [8] | Conde C. , Serrano A., Licesio J. Rodr´ıguez-Arag´on R., P´erez J., and Cabello E., An Experimental Approach to a Real-Time Controlled Traffic Light multi-Agent Application, In Procs. 3, 2004. |
| [9] | Nijhuis E., Peelen S., Schouten R. , SteingrÄover M., Project Design and Organization of Autonomous Systems: Intelligent Traffic Light Control, 2005. |
| [10] | Brockfeld D. K. E., Ringel J. M. J., Rössel C., Tuchscheerer W, Wagner P., Wösler R. Simulation of modern Traffic Lights Control Systems using the open source Traffic Simulation SUMO, Proceedings of the 3rd Industrial Simulation Conference, 2005. |
| [11] | Zurada J. M. Ed., Introduction to Artificial Neural Systems, West Publishing Company, 1996. |
| [12] | Fausett L.Ed., Fundamental of Neural Networks, Architectures, Algorithms and Applications. Prentice-Hall, 1994. |
| [13] | Abdul Kareem E I, Alsalihy W. A. H and Jantan A., Multi-Connect Architecture (MCA) Associative Memory: A Modified Hopfield Neural Network, Intelligent Automation and Soft Computing, Vol. 18, No. 3, pp. 291-308, 2012 Copyright ©, TSI® Press Printed in the USA, 2012. |
| [14] | Abdul Kaream E. I., Mutar k. N., Moussa H. A ,Gray Image Recognition Using Hopfield Neural Network With Multi- Bitplane and Multi-Connect Architecture. Proceedings of the international Conference on Computer Graphics, Imaging and Visualization (CGIV'06) IEEE, 2006. |
| [15] | Abdul Kaream E. I., Hopfield Neural Network Using Genetic Algorithm. M.Sc. Thesis, High studies institute for computer and information, Baghdad, Iraq, 2001. |
| [16] | Emad I Abdul Kareem, Aman Jantan (2011), "An Intelligent Traffic Light Monitor System using an Adaptive Associative Memory", IJIPM: International Journal of Information Processing and Management, Vol. 2, No. 2, pp. 23 ~ 39. |
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
Share This Article
Twitter
Linked In
Facebook
Copy
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 -
Email: