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Review of Traffic Signal Control based on Fuzzy Logic

by Nidhi Sharma, Shashank Sahu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 13
Year of Publication: 2016
Authors: Nidhi Sharma, Shashank Sahu
10.5120/ijca2016910869

Nidhi Sharma, Shashank Sahu . Review of Traffic Signal Control based on Fuzzy Logic. International Journal of Computer Applications. 145, 13 ( Jul 2016), 18-22. DOI=10.5120/ijca2016910869

@article{ 10.5120/ijca2016910869,
author = { Nidhi Sharma, Shashank Sahu },
title = { Review of Traffic Signal Control based on Fuzzy Logic },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 13 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 18-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number13/25339-2016910869/ },
doi = { 10.5120/ijca2016910869 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:48:45.223076+05:30
%A Nidhi Sharma
%A Shashank Sahu
%T Review of Traffic Signal Control based on Fuzzy Logic
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 13
%P 18-22
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the present time the number of private vehicles is increasing rapidly. As result the traffic congestion is growing and becoming a huge problem in big cities. Due to traffic congestion many huge problems are occurring like pollution, wastage of time, money, accidents etc. Traffic congestion occurred very frequently in the present time which affect the daily life and introduce so many problems and challenges. To solve all these problems traffic signal control is a best solution. Fuzzy set theory have been widely applied to many applications and it can easily deal with uncertainty, complexity and imprecision in many systems. The problems in traffic engineering are mainly characterized by the imprecise, ambiguous and uncertain parameters. Due to such type of parameters fuzzy logic method is best suitable approach for traffic signal control. The performance of fuzzy logic based controller is better for two one way street based on extension of green light time. Later fuzzy logic controller was developed for two way intersection without turns, single intersection with all movements, multiple intersection etc. Better performance of fuzzy logic based traffic signal control in comparison of traditional traffic signal control increase the research in this field. Fuzzy logic approach will definitely help in transportation management system in future.

References
  1. Zadeh, Lotfi A. "Fuzzy sets." Information and control 8.3 (1965): 338-353.
  2. Zadeh, Lotfi A. "The concept of a linguistic variable and its application to approximate reasoning—I." Information sciences 8.3 (1975): 199-249.
  3. Hoogendoorn, Serge Paul, G. Copinga, and U. Kaymak. Perspectives of fuzzy logic in traffic engineering: main document. Ministry of Transport, Public Works and Water Management, 1998.
  4. Robertson, Dennis I. "TRANSYT: a traffic network study tool." (1969).
  5. Hunt, P. B., D. I. Robertson, R. D. Bretherton, and R. I. Winton. SCOOT-a traffic responsive method of coordinating signals. No. LR 1014 Monograph. 1981
  6. Pappis, C. P., & Mamdani, E. H. (1977). A fuzzy logic controller for a trafc junction. Systems, Man and Cybernetics, IEEE Transactions on, 7(10), 707-717.
  7. Teodorović, Dušan. "Fuzzy logic systems for transportation engineering: the state of the art." Transportation Research Part A: Policy and Practice 33.5 (1999): 337-364.
  8. Zadeh, Lotfi Asker. "Fuzzy sets as a basis for a theory of possibility." Fuzzy sets and systems 1, no. 1 (1978): 3-28.
  9. Saeed, Yousaf, M. Saleem Khan, Khalil Ahmed, and A. Salam Mubashar. "A multi-agent based autonomous traffic lights control system using fuzzy control." International Journal of Scientific & Engineering Research 2, no. 6 (2011): 1.
  10. Niittymäki, Jarkko. "General fuzzy rule base for isolated traffic signal control‐rule formulation." Transportation Planning and Technology 24.3 (2001): 227-247.
  11. Wei, Wu, et al. "Traffic signal control using fuzzy logic and MOGA."Systems, Man, and Cybernetics, 2001 IEEE International Conference on. Vol. 2. IEEE, 2001.
  12. Qiao, Fengxiang, et al. "Fuzzy logic based intersection delay estimation."Mathematical and computer modelling 36.11 (2002): 1425-1434.
  13. Chou, Chih-Hsun, and Jen-Chao Teng. "A fuzzy logic controller for traffic junction signals." Information Sciences 143.1 (2002): 73-97.
  14. Kosonen, Iisakki. "Multi-agent fuzzy signal control based on real-time simulation." Transportation Research Part C: Emerging Technologies 11.5 (2003): 389-403.
  15. Niittymäki, Jarkko, and Esko Turunen. "Traffic signal control on similarity logic reasoning." Fuzzy Sets and Systems 133.1 (2003): 109-131.
  16. Zhang, Lin, Honglong Li, and Panos D. Prevedouros. "Signal control for oversaturated intersections using fuzzy logic." Transportation Research Board Annual Meeting, Washington DC, USA. 2005.
  17. Bazzan, Ana LC. "A distributed approach for coordination of traffic signal agents." Autonomous Agents and Multi-Agent Systems 10.1 (2005): 131-164.
  18. Zang, Lilin, Lei Jia, and Zhongqin Lin. "Research of traffic signal control and simulation based on fuzzy logic." Journal of Highway and Transportation Research and Development (English Edition) 1.1 (2006): 96-99.
  19. Dimitriou, Loukas, Theodore Tsekeris, and Antony Stathopoulos. "Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow." Transportation Research Part C: Emerging Technologies 16.5 (2008): 554-573.
  20. Zarandi, Mohammad Hossein Fazel, and Shabnam Rezapour. "A fuzzy signal controller for isolated intersections." Journal of Uncertain Systems 3.3 (2009): 174-182.
  21. Gokulan, Balaji Parasumanna, and Dipti Srinivasan. "Distributed geometric fuzzy multiagent urban traffic signal control." Intelligent Transportation Systems, IEEE Transactions on 11.3 (2010): 714-727.
  22. Balaji, P. G., and Dipti Srinivasan. "Multi-agent system in urban traffic signal control." Computational Intelligence Magazine, IEEE 5.4 (2010): 43-51.
  23. Aksaç, Alper, Erkam Uzun, and Tansel Özyer. "A real time traffic simulator utilizing an adaptive fuzzy inference mechanism by tuning fuzzy parameters." Applied Intelligence 36, no. 3 (2012): 698-720.
  24. Zaied, Abdel Nasser H., and Woroud Al Othman. "Development of a fuzzy logic traffic system for isolated signalized intersections in the State of Kuwait." Expert Systems with Applications 38.8 (2011): 9434-9441.
  25. Taha, Mohammad A., and Laheeb Ibrahim. "Traffic simulation system based on fuzzy logic." Procedia Computer Science 12 (2012): 356-360.
  26. Bi , Yunrui, et al. "Type-2 fuzzy multi-intersection traffic signal control with differential evolution optimization." Expert Systems with Applications 41.16 (2014): 7338-7349.
  27. Niittymaki, Jarkko, and Shinya Kikuchi. "Application of fuzzy logic to the control of a pedestrian crossing signal." Transportation Research Record: Journal of the Transportation Research Board 1651 (1998): 30-38.
Index Terms

Computer Science
Information Sciences

Keywords

Traffic signal control fuzzy logic

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