Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS

Kingsley Monday Udofia; Joy Omoavowere Emagbetere; Frederick Obataimen Edeko

doi:doi:10.11648/j.acis.20140202.12

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Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS

Kingsley Monday Udofia, Joy Omoavowere Emagbetere, Frederick Obataimen Edeko

Published in Automation, Control and Intelligent Systems (Volume 2, Issue 2)

Received: 20 March 2014 Accepted: 10 April 2014 Published: 20 May 2014

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Abstract

This paper presents a traffic signal phase sequencing using adaptive neuro-fuzzy inference system (ANFIS) technique. The system is designed to emulate traffic expert on the selection of the appropriate phase to be given right-of-way at an isolated intersection based on the prevailing traffic situation. Inputs (queuelength and waiting time of vehicles) from traffic detectors are used to determine the selection of the next green phase. We evaluated the developed model for five different common traffic scenarios using MATLAB. The results obtained indicates that the developed model adaptively and effectively selects a phase to be given next green signal after considering the traffic situation and the nature of the intersection in question.

Published in	Automation, Control and Intelligent Systems (Volume 2, Issue 2)
DOI	10.11648/j.acis.20140202.12
Page(s)	21-26
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

Keywords

Adaptive, Neuro-Fuzzy Inference System, Phase Sequencing, Vehicle Traffic Control, Isolated Intersection

References

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[2]	G.E.M.D.C. Bandara et at.: Application of fuzzy logic in intel-ligent traffic control systems, National University of Singapore, CIRAS, 2003
[3]	M. R. A. Pur-nomo et al.: Development of a low cost smart traffic controller system, European Journal of Scientific Research, 2009, 32(4): 490 – 499
[4]	Niittymaki J. et al.: Fuzzy Traffic Signal Control and a New Interface Method - Maximal Fuzzy Similarity. In: Proc., The 13th Mini-EURO Conf. (Handling Uncertainty in the Analysis of Traffic and Transportation Systems) and the 9th Mtg. EURO Working Group on Transportation Intermodality, Sustainability and Intelligent Transporta-tion Systems, Bari, Italy, 2002, 716–728.
[5]	Zhang L, Li H, Prevedouros P D. Signal control for oversaturated intersections using fuzzy logic. In: Proc. of 84th Transp. Res. Bd. Ann. Mtg., Wash-ington, D.C., 2005
[6]	Nakatsuyama M, Nagahashi H, Nishizuka N. Fuzzy logic phase control-ler for traffic junctions in the one-way arterial road. In: Proc., IFAC 9th Triennial World Cong., Bu-dapest, Hungary, 1984, 2865–2870
[7]	Tan, K.K., Khalid, M., Yusof, R.: Intelligent Traffic Lights Control by Fuzzy Logic. Malaysian Journal of Computer Science 9(2), 29–35 (1996)
[8]	Pappis C. and Mamdani E.: A fuzzy logic controller for a traffic junction, IEEE Trans. Systems, Man, and Cybernetics SMC-7, 1977, 7(10): 707–717.
[9]	J.S.R. Jang: ANFIS: Adaptive-network-based Fuzzy Inference Systems. IEEE Trans, Syst, Man Cybern., 23(3), pp. 665–685, 1993.
[10]	Wenteng M.: A Real-time Performance Measurement System for Arterial Traffic Signals. A Ph.D thesis, Graduate School, University of Minnesota, 2008.

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APA Style

Kingsley Monday Udofia, Joy Omoavowere Emagbetere, Frederick Obataimen Edeko. (2014). Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS. Automation, Control and Intelligent Systems, 2(2), 21-26. https://doi.org/10.11648/j.acis.20140202.12

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ACS Style

Kingsley Monday Udofia; Joy Omoavowere Emagbetere; Frederick Obataimen Edeko. Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS. Autom. Control Intell. Syst. 2014, 2(2), 21-26. doi: 10.11648/j.acis.20140202.12

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AMA Style

Kingsley Monday Udofia, Joy Omoavowere Emagbetere, Frederick Obataimen Edeko. Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS. Autom Control Intell Syst. 2014;2(2):21-26. doi: 10.11648/j.acis.20140202.12

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@article{10.11648/j.acis.20140202.12,
  author = {Kingsley Monday Udofia and Joy Omoavowere Emagbetere and Frederick Obataimen Edeko},
  title = {Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS},
  journal = {Automation, Control and Intelligent Systems},
  volume = {2},
  number = {2},
  pages = {21-26},
  doi = {10.11648/j.acis.20140202.12},
  url = {https://doi.org/10.11648/j.acis.20140202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20140202.12},
  abstract = {This paper presents a traffic signal phase sequencing using adaptive neuro-fuzzy inference system (ANFIS) technique. The system is designed to emulate traffic expert on the selection of the appropriate phase to be given right-of-way at an isolated intersection based on the prevailing traffic situation. Inputs (queuelength and waiting time of vehicles) from traffic detectors are used to determine the selection of the next green phase. We evaluated the developed model for five different common traffic scenarios using MATLAB. The results obtained indicates that the developed model adaptively and effectively selects a phase to be given next green signal after considering the traffic situation and the nature of the intersection in question.},
 year = {2014}
}

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TY  - JOUR
T1  - Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS
AU  - Kingsley Monday Udofia
AU  - Joy Omoavowere Emagbetere
AU  - Frederick Obataimen Edeko
Y1  - 2014/05/20
PY  - 2014
N1  - https://doi.org/10.11648/j.acis.20140202.12
DO  - 10.11648/j.acis.20140202.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 21
EP  - 26
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20140202.12
AB  - This paper presents a traffic signal phase sequencing using adaptive neuro-fuzzy inference system (ANFIS) technique. The system is designed to emulate traffic expert on the selection of the appropriate phase to be given right-of-way at an isolated intersection based on the prevailing traffic situation. Inputs (queuelength and waiting time of vehicles) from traffic detectors are used to determine the selection of the next green phase. We evaluated the developed model for five different common traffic scenarios using MATLAB. The results obtained indicates that the developed model adaptively and effectively selects a phase to be given next green signal after considering the traffic situation and the nature of the intersection in question.
VL  - 2
IS  - 2
ER  -

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Author Information

Kingsley Monday Udofia

Department of Elect/Elect/Computer Engineering, University of Uyo, Uyo, Nigeria
Joy Omoavowere Emagbetere

Department of Electrical/Electronic Engineering, University of Benin, Benin City, Nigeria
Frederick Obataimen Edeko

Department of Electrical/Electronic Engineering, University of Benin, Benin City, Nigeria

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Plain Text BibTeX RIS

APA Style

Kingsley Monday Udofia, Joy Omoavowere Emagbetere, Frederick Obataimen Edeko. (2014). Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS. Automation, Control and Intelligent Systems, 2(2), 21-26. https://doi.org/10.11648/j.acis.20140202.12

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ACS Style

Kingsley Monday Udofia; Joy Omoavowere Emagbetere; Frederick Obataimen Edeko. Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS. Autom. Control Intell. Syst. 2014, 2(2), 21-26. doi: 10.11648/j.acis.20140202.12

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AMA Style

Kingsley Monday Udofia, Joy Omoavowere Emagbetere, Frederick Obataimen Edeko. Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS. Autom Control Intell Syst. 2014;2(2):21-26. doi: 10.11648/j.acis.20140202.12

Copy | Download

@article{10.11648/j.acis.20140202.12,
  author = {Kingsley Monday Udofia and Joy Omoavowere Emagbetere and Frederick Obataimen Edeko},
  title = {Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS},
  journal = {Automation, Control and Intelligent Systems},
  volume = {2},
  number = {2},
  pages = {21-26},
  doi = {10.11648/j.acis.20140202.12},
  url = {https://doi.org/10.11648/j.acis.20140202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20140202.12},
  abstract = {This paper presents a traffic signal phase sequencing using adaptive neuro-fuzzy inference system (ANFIS) technique. The system is designed to emulate traffic expert on the selection of the appropriate phase to be given right-of-way at an isolated intersection based on the prevailing traffic situation. Inputs (queuelength and waiting time of vehicles) from traffic detectors are used to determine the selection of the next green phase. We evaluated the developed model for five different common traffic scenarios using MATLAB. The results obtained indicates that the developed model adaptively and effectively selects a phase to be given next green signal after considering the traffic situation and the nature of the intersection in question.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Dynamic Traffic Signal Phase Sequencing for an Isolated Intersection Using ANFIS
AU  - Kingsley Monday Udofia
AU  - Joy Omoavowere Emagbetere
AU  - Frederick Obataimen Edeko
Y1  - 2014/05/20
PY  - 2014
N1  - https://doi.org/10.11648/j.acis.20140202.12
DO  - 10.11648/j.acis.20140202.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 21
EP  - 26
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20140202.12
AB  - This paper presents a traffic signal phase sequencing using adaptive neuro-fuzzy inference system (ANFIS) technique. The system is designed to emulate traffic expert on the selection of the appropriate phase to be given right-of-way at an isolated intersection based on the prevailing traffic situation. Inputs (queuelength and waiting time of vehicles) from traffic detectors are used to determine the selection of the next green phase. We evaluated the developed model for five different common traffic scenarios using MATLAB. The results obtained indicates that the developed model adaptively and effectively selects a phase to be given next green signal after considering the traffic situation and the nature of the intersection in question.
VL  - 2
IS  - 2
ER  -

Copy | Download