Traffic Light Controller Module Based on Particle Swarm Optimization (PSO)

Emad Issa Abdul Kareem; Ayat Ismail Mejbel

doi:doi:10.11648/j.ajai.20180201.12

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Traffic Light Controller Module Based on Particle Swarm Optimization (PSO)

Emad Issa Abdul Kareem, Ayat Ismail Mejbel

Published in American Journal of Artificial Intelligence (Volume 2, Issue 1)

Received: 22 February 2018 Accepted: 10 March 2018 Published: 12 April 2018

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Abstract

A traffic light control module base on PSO algorithm has been presented to find the optimal set of adjacent streets that are the candidate to take the green period time providing the best vehicles flow. In our previous work a visual traffic light monitoring module has been presented. This module able to determine the traffic conditions (crowded, normal and empty). The proposed control module should be able to integrate with the previous monitoring module to develop a new complete intelligent traffic light system. Promising results have been obtained via applying the proposed traffic light controller module. The controller module shows its ability to select a set of streets. The green period time will be given to these selected streets to achieve the optimal vehicle flow through the traffic light’s intersections. The results show that the proposed control module improving the flow ratio about 85% to 96% with a different number of traffic lights.

Published in	American Journal of Artificial Intelligence (Volume 2, Issue 1)
DOI	10.11648/j.ajai.20180201.12
Page(s)	7-15
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), 2018. Published by Science Publishing Group

Keywords

Transportation System, Traffic Light Controller System, Particle Swarm Optimization (PSO)

References

[1]	Marco Wiering, Jelle van Veenen, Jilles Vreeken and Arne Koopman, "Intelligent Traffic Light Control, Intelligent Systems Group", Institute of Information and Computing Sciences Utrecht University Padualaan 14, 3508TB Utrecht, The Netherlands, July 9, 2004.
[2]	Avram Robinson-Mosher, Christoper Egner, Learning Traffic Light Control Policies.
[3]	S. K. Subramaniam, V. R. Gannapathy, S. A. Anas, A. B. M. Diah, M. K. Suaidi, and A. Hamidon, "Intelligent and Self Control Safety Traffic Light System for Road Constructions, Proceedings of 8th WSEAS International Conference on Circuits", Systems, Electronics, Control and Signal Processing, December 14- 16, 2009, pp. 53-58.
[4]	V. R Gannapathy, S. K Subramaniam, A. B Mohamad Diah, M. K Suaidi and A. H Hamidon, "Risk Factors in a Road Construction Site, Proceedings of the World Academy of Science", Engineering and Technology 46, 2008, pp. 640–643.
[5]	Hazem Ahmed, Janice Glasgow “Swarm Intelligence: Concepts, Models and Applications” Conference: Queen's University, School of Computing Technical Reports, At Kingston, Canada, Volume: Technical Report 2012-585, February 2012, DOI 10.13140/2.1.1320.2568.
[6]	Bijaya Ketan Panigrahi, Swagatam Das, Ponnuthurai Nagaratnam Suganthan and Subhransu Sekhar Dash," Swarm, Evolutionary and Memetic Computing". Springer-Verlag Berlin Heidelberg, ISSN 0302-9743, 2010.
[7]	Faez Hassan Ali, " Improving Exact and Local Search Algorithms for Solving Some Combinatorial Optimization Problems ". Ph.D thesis, al-mustansiriya university college of science, department of mathematics, 2015.
[8]	Singh. Y. P. "Analysis and Designing of Proposed Intelligent Road Traffic Congestion Control System with Image Mosaicking Technique". International Journal of IT, Engineering and Applied Sciences Research (IJIEASR). 2013; 2 (4).
[9]	Salehi M, Sepahvand I, and Yarahmadi M. TLCSBFL: "A Traffic Lights Control System Based on Fuzzy Logic". International Journal of u- and e- Service, Science and Technology. 2014; 7 (3).
[10]	Hassan Z. "Prototype software Agent for solving a traffic light problem." Journal of Babylon University. 2014; 22 (2).
[11]	Walad K, Shetty J. "Traffic Light Control System Using Image Processing”. International Journal of Innovative Research in Computer and Communication Engineering. 2014; 2 (5).
[12]	Ezzat A, Farouk H, El-Kilany K and Abdelmoneim A. "Development of a Stochastic Genetic Algorithm for Trafﬁc Signal Timings Optimization". Proceedings of the 2014 Industrial and Systems Engineering Research Conference. 2014.
[13]	Abdul Kareem E and Abbas S and Mahmood S. "Intelligent traffic light controller based on MCA associative memory". Science Journal of Circuits, Systems and Signal Processing. 2014; 3 (6-1).
[14]	Adham A and Abdul Rahman K, Hayyan M. "An Integrated Model to Enhance the Transportation Methods in Malaysia: Review Paper". Journal of Applied Science and Agriculture. 2014; 9 (18).
[15]	Abdul Rahman K. "An Artificial Intelligence Techniques and Simulation Model to Control a Traffic Jam System in Malaysia". Asian Journal of Business and Management. 2016; 4 (1).
[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.
[17]	Permit Writers Workshop, Signal Timing, http://cce.oregonstate.edu/sites/cce.oregonstate.edu/files/pw_sigtime.pdf, last visit 2017.
[18]	© Springer International Publishing AG 2017 A. Kaveh, Advances in Metaheuristic Algorithms for Optimal Design of Structures, DOI 10.1007/978-3-319-46173-1_2.
[19]	Kennedy J (2006) Swarm intelligence. In: Handbook of nature-inspired and innovative computing. Springer, New York, pp 187–219.
[20]	Talbi EG (2009) Metaheuristics: from design to implementation. Wiley, UK.
[21]	N. Kham, and C. Nwe, “Implementation of modern traffic light control system”, International journal of scientific and research publications, Vol. 4, Issue 6, Jun. 2014.

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APA Style

Emad Issa Abdul Kareem, Ayat Ismail Mejbel. (2018). Traffic Light Controller Module Based on Particle Swarm Optimization (PSO). American Journal of Artificial Intelligence, 2(1), 7-15. https://doi.org/10.11648/j.ajai.20180201.12

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ACS Style

Emad Issa Abdul Kareem; Ayat Ismail Mejbel. Traffic Light Controller Module Based on Particle Swarm Optimization (PSO). Am. J. Artif. Intell. 2018, 2(1), 7-15. doi: 10.11648/j.ajai.20180201.12

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AMA Style

Emad Issa Abdul Kareem, Ayat Ismail Mejbel. Traffic Light Controller Module Based on Particle Swarm Optimization (PSO). Am J Artif Intell. 2018;2(1):7-15. doi: 10.11648/j.ajai.20180201.12

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@article{10.11648/j.ajai.20180201.12,
  author = {Emad Issa Abdul Kareem and Ayat Ismail Mejbel},
  title = {Traffic Light Controller Module Based on Particle Swarm Optimization (PSO)},
  journal = {American Journal of Artificial Intelligence},
  volume = {2},
  number = {1},
  pages = {7-15},
  doi = {10.11648/j.ajai.20180201.12},
  url = {https://doi.org/10.11648/j.ajai.20180201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20180201.12},
  abstract = {A traffic light control module base on PSO algorithm has been presented to find the optimal set of adjacent streets that are the candidate to take the green period time providing the best vehicles flow. In our previous work a visual traffic light monitoring module has been presented. This module able to determine the traffic conditions (crowded, normal and empty). The proposed control module should be able to integrate with the previous monitoring module to develop a new complete intelligent traffic light system. Promising results have been obtained via applying the proposed traffic light controller module. The controller module shows its ability to select a set of streets. The green period time will be given to these selected streets to achieve the optimal vehicle flow through the traffic light’s intersections. The results show that the proposed control module improving the flow ratio about 85% to 96% with a different number of traffic lights.},
 year = {2018}
}

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TY - JOUR
T1 - Traffic Light Controller Module Based on Particle Swarm Optimization (PSO)
AU - Emad Issa Abdul Kareem
AU - Ayat Ismail Mejbel
Y1 - 2018/04/12
PY - 2018
N1 - https://doi.org/10.11648/j.ajai.20180201.12
DO - 10.11648/j.ajai.20180201.12
T2 - American Journal of Artificial Intelligence
JF - American Journal of Artificial Intelligence
JO - American Journal of Artificial Intelligence
SP - 7
EP - 15
PB - Science Publishing Group
SN - 2639-9733
UR - https://doi.org/10.11648/j.ajai.20180201.12
AB - A traffic light control module base on PSO algorithm has been presented to find the optimal set of adjacent streets that are the candidate to take the green period time providing the best vehicles flow. In our previous work a visual traffic light monitoring module has been presented. This module able to determine the traffic conditions (crowded, normal and empty). The proposed control module should be able to integrate with the previous monitoring module to develop a new complete intelligent traffic light system. Promising results have been obtained via applying the proposed traffic light controller module. The controller module shows its ability to select a set of streets. The green period time will be given to these selected streets to achieve the optimal vehicle flow through the traffic light’s intersections. The results show that the proposed control module improving the flow ratio about 85% to 96% with a different number of traffic lights.
VL - 2
IS - 1
ER -

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Author Information

Emad Issa Abdul Kareem

Department of Computer Sciences, Mustansiriyah University, Baghdad, Iraq
Ayat Ismail Mejbel

Department of Computer Sciences, Mustansiriyah University, Baghdad, Iraq

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APA Style

Emad Issa Abdul Kareem, Ayat Ismail Mejbel. (2018). Traffic Light Controller Module Based on Particle Swarm Optimization (PSO). American Journal of Artificial Intelligence, 2(1), 7-15. https://doi.org/10.11648/j.ajai.20180201.12

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ACS Style

Emad Issa Abdul Kareem; Ayat Ismail Mejbel. Traffic Light Controller Module Based on Particle Swarm Optimization (PSO). Am. J. Artif. Intell. 2018, 2(1), 7-15. doi: 10.11648/j.ajai.20180201.12

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AMA Style

Emad Issa Abdul Kareem, Ayat Ismail Mejbel. Traffic Light Controller Module Based on Particle Swarm Optimization (PSO). Am J Artif Intell. 2018;2(1):7-15. doi: 10.11648/j.ajai.20180201.12

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@article{10.11648/j.ajai.20180201.12,
  author = {Emad Issa Abdul Kareem and Ayat Ismail Mejbel},
  title = {Traffic Light Controller Module Based on Particle Swarm Optimization (PSO)},
  journal = {American Journal of Artificial Intelligence},
  volume = {2},
  number = {1},
  pages = {7-15},
  doi = {10.11648/j.ajai.20180201.12},
  url = {https://doi.org/10.11648/j.ajai.20180201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20180201.12},
  abstract = {A traffic light control module base on PSO algorithm has been presented to find the optimal set of adjacent streets that are the candidate to take the green period time providing the best vehicles flow. In our previous work a visual traffic light monitoring module has been presented. This module able to determine the traffic conditions (crowded, normal and empty). The proposed control module should be able to integrate with the previous monitoring module to develop a new complete intelligent traffic light system. Promising results have been obtained via applying the proposed traffic light controller module. The controller module shows its ability to select a set of streets. The green period time will be given to these selected streets to achieve the optimal vehicle flow through the traffic light’s intersections. The results show that the proposed control module improving the flow ratio about 85% to 96% with a different number of traffic lights.},
 year = {2018}
}

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