American Journal of Artificial Intelligence
Volume 1, Issue 1, December 2017, Pages: 36-43
Received: May 1, 2017;
Accepted: May 26, 2017;
Published: Aug. 3, 2017
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Okene David Ese, Department of Electrical and Electronics Engineering, Federal University of Petroleum Resources, Effurun, Nigeria
Okhueleigbe Emmanuel Ighodalo, Department of Electrical and Electronics Engineering, Federal University of Petroleum Resources, Effurun, Nigeria
Current traffic light systems use a fixed time delay for different traffic directions and do follow a particular cycle while switching from one signal to another. This creates unwanted congestion during peak hours, loss of man-hours and eventually decline in productivity. In addition to this, the current traffic light systems encourage extortion by corrupt traffic officials as commuters often violate traffic rules because of the insufficient time allocated to their lanes or may want to avoid a long waiting period for their lanes to come up. This research is aimed at tackling the afore-mentioned problems by adopting a density based traffic control approach using Jakpa Junction, one of the busiest junctions in Delta State, Nigeria as a case study. The developed system uses a microcontroller of PIC89C51 microcontroller duly interfaced with sensors. The signal timing changes automatically based on the traffic density at the junction, thereby, avoiding unnecessary waiting time at the junction. The sensors used in this project were infra-red (IR) sensors and photodiodes which were placed in a Line of Sight configuration across the loads to detect the density of the traffic signal. The density of the vehicles is measured in three zones i.e., low, medium and high based on which timings were allotted accordingly. The developed system has proven to be smart and intelligent and capable of curbing incidences of traffic malpractices and inefficiencies that have been the bane of current traffic congestion control systems in emerging cities of the third world.
Okene David Ese,
Okhueleigbe Emmanuel Ighodalo,
An Intelligent System for Traffic Control in Smart Cities: A Case Study, American Journal of Artificial Intelligence.
Vol. 1, No. 1,
2017, pp. 36-43.
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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