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 :

1. 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
2. “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.
3. E. Transportation Team, “The Cost of Congestion to the Economy of the Portland Region,” 2005.
4. 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.
5. 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
6. 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
7. M. mueen ul Islam, R. B. N, and S. B. N, “Traffic management using Image Processing,” International Research Journal of Engineering and Technology, p. 4363, 2008, [Online]. Available: www.irjet.net
8. M. Tubaishat, Y. Shang, and H. Shi, “Adaptive traffic light control with wireless sensor networks,” in 2007 4th Annual IEEE Consumer Communications and Networking Conference, CCNC 2007, 2007, pp. 187–191. doi: 10.1109/CCNC.2007.44.
9. 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.
10. 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.
11. 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
12. G. M. H. Amer and A. M. Abushaala, “Edge detection methods,” in 2015 2nd World Symposium on Web Applications and Networking, WSWAN 2015, Institute of Electrical and Electronics Engineers Inc., Aug. 2015. doi: 10.1109/WSWAN.2015.7210349.
13. “Canny Edge Detection 09gr820,” 2009.
14. J. Seo, S. Chae, J. Shim, D. Kim, C. Cheong, and T. D. Han, “Fast contour-tracing algorithm based on a pixel-following method for image sensors,” Sensors (Switzerland), vol. 16, no. 3, Mar. 2016, doi: 10.3390/s16030353.
15. A. D. Rana, J. B. Darji, M. Pandya, P. G. Student, E. E. Department, and A.’ Bad, “Backward / Forward Sweep Load Flow Algorithm for Radial Distribution System,” 2014. [Online]. Available: www.ijsrd.com
16. M. Safaldin, N. Zaghden, and M. Mejdoub, “Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. An Improved YOLOv8 to Detect Moving Objects”, doi: 10.1109/ACCESS.2023.0322000.
17. 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.”