Authors :
Rodreck Shazhu; Tawanda Mudawarima
Volume/Issue :
Volume 9 - 2024, Issue 6 - June
Google Scholar :
https://shorturl.at/acfPx
Scribd :
https://tinyurl.com/yt3vy46x
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24JUN2018
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
This research presents a novel dynamic traffic
light algorithm designed to optimize traffic flow and
reduce traffic congestion by dynamically allocating green
time based on post-intersection space availability. The
algorithm employs a three-stage process: input
generation, processing, and output. The input stage
involves capturing traffic images using cameras
strategically placed at intersections, which are then
processed using background subtraction, edge detection,
and object counting techniques. The processing phase
includes vehicle counting using the YOLOv8 algorithm
and open space calculation based on the maximum
capacity of each road section. The output phase involves
dynamically allocating green time to roads based on
available post-intersection space and occupancy rates.
The algorithm is designed to adapt to changing traffic
conditions by continuously monitoring the post-
intersection space and adjusting green times accordingly.
It also incorporates a reset timer to ensure the algorithm
loops back to the initial stage of gathering and processing
traffic images. Simulation experiments using a physical
model with toy vehicles and a camera setup
demonstrated the benefits of this approach. Compared to
the density-based approaches[1], this algorithm reduced
average vehicle delay by 20-30%, increased overall
intersection throughput by 15-25%, and decreased
maximum queue lengths in each lane by 25-35%. It also
adapted more effectively to fluctuations in traffic
conditions, improving performance metrics by 20-30%.
These results highlight the potential of incorporating
downstream space considerations into traffic light
control algorithms to enhance intersection efficiency,
reduce traffic congestion, and enable more adaptive and
fair traffic management.
Keywords :
Traffic Congestion, Post-Intersection, Pre- Intersection, Traffic Light, Dynamic Traffic Light, Algorithm, Sensor, Image, Wireless
References :
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- “Approaches for Reducing Urban Traffic Congestion in the City of Harare,” Journal of Economics and Sustainable Development, Feb. 2020, doi: 10.7176/jesd/11-4-01.
- E. Transportation Team, “The Cost of Congestion to the Economy of the Portland Region,” 2005.
- M. R. Sathuluri, S. K. Bathula, P. Yadavalli, and R. Kandula, “IMAGE processing based intelligent traffic controlling and monitoring system using Arduino,” in 2016 International Conference on Control Instrumentation Communication and Computational Technologies, ICCICCT 2016, Institute of Electrical and Electronics Engineers Inc., Jul. 2017, pp. 393–396. doi: 10.1109/ICCICCT.2016.7987980.
- E. Faruk Bin Poyen, A. Kumar Bhakta, Bd. Manohar, I. Ali, and A. Rao, “Density Based Traffic Control,” International Journal of Advanced Engineering, Management and Science (IJAEMS), vol. 2, no. 8, 2016, [Online]. Available: www.ijaems.com
- R. V Kulkarni, S. R. Bhadane, and P. A. Gardi, “SMART TRAFFIC CONTROLLER USING IMAGE PROCESSING,” International Research Journal of Engineering and Technology, 2020, [Online]. Available: www.irjet.net
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- W. N. S. F. W. Ariffin et al., “Real-time Dynamic Traffic Light ControlSystem with Emergency Vehicle Priority,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jun. 2021. doi: 10.1088/1742-6596/1878/1/012063.
- O. Sow, Y. Traore, M. Andallah Diop, A. Sadikh Faye, J. Ndiaye, and A. Diop, “SMART TRAFFIC LIGHTS USING IOT TECHNOLOGIES: PROJECT CONCEPT AND PROGRESS,” Int J Adv Res (Indore), vol. 12, no. 01, pp. 201–211, Jan. 2024, doi: 10.21474/IJAR01/18110.
- M. Bhatia, A. Aggarwal, M. Singh Bhatia, and N. Kumar, “Smart Traffic Light System to Control Traffic Congestion PJAEE, 17 (9) (2020) Smart Traffic Light System to Control Traffic Congestion.” [Online]. Available: https://www.researchgate.net/publication/348805113
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- C. Roman, R. Liao, P. Ball, S. Ou, and M. De Heaver, “Detecting On-Street Parking Spaces in Smart Cities: Performance Evaluation of Fixed and Mobile Sensing Systems.”
This research presents a novel dynamic traffic
light algorithm designed to optimize traffic flow and
reduce traffic congestion by dynamically allocating green
time based on post-intersection space availability. The
algorithm employs a three-stage process: input
generation, processing, and output. The input stage
involves capturing traffic images using cameras
strategically placed at intersections, which are then
processed using background subtraction, edge detection,
and object counting techniques. The processing phase
includes vehicle counting using the YOLOv8 algorithm
and open space calculation based on the maximum
capacity of each road section. The output phase involves
dynamically allocating green time to roads based on
available post-intersection space and occupancy rates.
The algorithm is designed to adapt to changing traffic
conditions by continuously monitoring the post-
intersection space and adjusting green times accordingly.
It also incorporates a reset timer to ensure the algorithm
loops back to the initial stage of gathering and processing
traffic images. Simulation experiments using a physical
model with toy vehicles and a camera setup
demonstrated the benefits of this approach. Compared to
the density-based approaches[1], this algorithm reduced
average vehicle delay by 20-30%, increased overall
intersection throughput by 15-25%, and decreased
maximum queue lengths in each lane by 25-35%. It also
adapted more effectively to fluctuations in traffic
conditions, improving performance metrics by 20-30%.
These results highlight the potential of incorporating
downstream space considerations into traffic light
control algorithms to enhance intersection efficiency,
reduce traffic congestion, and enable more adaptive and
fair traffic management.
Keywords :
Traffic Congestion, Post-Intersection, Pre- Intersection, Traffic Light, Dynamic Traffic Light, Algorithm, Sensor, Image, Wireless