CFP last date
01 August 2024
Reseach Article

Intelligent Vehicular Traffic Light Control using Hidden Markov Model

by Dominic Asamoah, Samuel Winful, Stephen Opoku Oppong
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 5
Year of Publication: 2017
Authors: Dominic Asamoah, Samuel Winful, Stephen Opoku Oppong

Dominic Asamoah, Samuel Winful, Stephen Opoku Oppong . Intelligent Vehicular Traffic Light Control using Hidden Markov Model. Communications on Applied Electronics. 7, 5 ( Aug 2017), 12-20. DOI=10.5120/cae2017652668

@article{ 10.5120/cae2017652668,
author = { Dominic Asamoah, Samuel Winful, Stephen Opoku Oppong },
title = { Intelligent Vehicular Traffic Light Control using Hidden Markov Model },
journal = { Communications on Applied Electronics },
issue_date = { Aug 2017 },
volume = { 7 },
number = { 5 },
month = { Aug },
year = { 2017 },
issn = { 2394-4714 },
pages = { 12-20 },
numpages = {9},
url = { },
doi = { 10.5120/cae2017652668 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T20:01:31.871500+05:30
%A Dominic Asamoah
%A Samuel Winful
%A Stephen Opoku Oppong
%T Intelligent Vehicular Traffic Light Control using Hidden Markov Model
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 5
%P 12-20
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Traffic management continues to remains a major problem in big cities. Allocating limited resources, i.e. roads, to an increasing number of users with individual needs and objectives, turns out to be a highly complex in most cases. This research uses Hidden Markov Model (HMM) as a component with unsupervised clustering scheme to determine the traffic situation of a road in a traffic video. An unsupervised clustering algorithm called Autoclass is applied to obtain the traffic density state (free, normal and congested) on motion features which are extracted from each frame. The three HMM models are constructed for each traffic state with each cluster corresponding to a state in the HMM. The result show that this method can handle varying illumination and classify traffic density in a (Region of Interest) ROI of a traffic video.

  1. Hmm M., 2011. Ghana Web. Retrieved September 30, 2012, from Ghana Web Site:
  2. Jarašūnienė A. 2004. “The Importance of Development of new technological systems in Transport means”, p. 233−236
  3. Jarašūnienė A. 2007 “Research into intelligent transport system (ITS) technologies and efficiency”, Journal of Vilnius Gediminas technical university, Lithuanian Academy of Science., Vol. XXII, No 2 (ISSN 1648-4142), 61–69.
  4. Patel M. and Ranganathan N. 1996. “An Intelligent System Architecture for Urban Traffic Control Application”
  5. Patel M. and Ranganathan N. 1996. “An Intelligent Decision-Making System for Urban Traffic-Control Application”, IDUTC
  6. Verma V. et al, 2008. “Intelligent transport management system using sensor networks”, IEEE Intelligent Vehicle Symposium Eindhoven University of Technology, pp. 4-6.
  7. Williams G., Tuytelaars T., & Gooll L., 2008. “An efficient dense and scale invariant spatio temporal inerest point detector, International Conference on Advance Information Networking and Applications, pp. 56-63.
  8. Chang B., 2008. “Wireless Sensor Network-based Adaptive Vehicle Navigation in Multihop-Relay”, WiMAX Networks. IEEE AINA.
  9. Malik T., 2008. Wireless Sensor-Based Traffic Light Control. Las Vegas, NV., IEEE Consumer Communications and Networking Conference
  10. Zou Y. et al, 2009. “Traffic Incident classification at intersections based on image sequences by HMM/SVM classifiers”, IEEE International Conference on hybrid Intelligent Systems
  11. Yang F. et al, 2004. “Online Recursive Algorithm For Short-Term Traffic Prediction”. Transportation Research Record No 1879, Transportation Research Board, 1-8.
  12. Xia J. and Chen M., 2009. “Dynamic Freeway Corridor Travel Time Prediction Using Single Inductive Loopetector Data”. Transportation Research Board, Transportation Research Board 85th Annual Meeting, Washington D.C,
  13. Ishak S. and Al-Deek H., 1856. “Statistical Evaluation of 1-4 Traffic Prediction System”. Transportation Research Record, 16-24.
  14. Kaveh F., 2010. Evaluating Moving average Techniques in Short-Term Travel Time Using an AVI Data Set, Transportation Research Board Annual Meeting, Washington D.C., 10-3144
  15. Vlahogianni.E., 2006. K. M. Patterns in short-term Urban Traffic Flow: A Framework for Statistical Detection and Identification. Transportation Research Board 85th Annual Meetings, Washington D. C.
  16. Tan M., 2009. “An Aggregation Approach to Short-Term Traffic Flow Prediction”. IEEE Transactions on Intelligent Transportation System, 10(1), 60-69.
  17. Rabiner L., 1989. “A tutorial on hidden Markov models and selected applications in speech recognition”. Proc. IEEE 77, 257–286.
  18. Sun, S., C.Z, 2006. ”A Bayesian network approach to traffic flow forecasting”. IEEE Trans. Intell. Transp. Syst.7(1), pp. 124-132.
  19. Jain V., 2012. Traffic density estimation for noisy camera sources. TRB 91st Annual Meeting, Washington D C.
  20. Papageorgiou M., 2003. “Review of road traffic control strategies”. Proceedings of the IEEE, 91 (12), pp. 2043-2067.
Index Terms

Computer Science
Information Sciences


Traffic management unsupervised clustering Hidden Markov Model Autoclass traffic density