Enhancing Traffic Flow Using Computer Vision Based - Dynamic Traffic Light Control and Lane Management

Main Article Content

Rajesh Phursule
Dhirajkumar Lal
Sandhya Waghere
Mohammad Abdul Mughni
Sarvesh Ransubhe
Chinmay Shiralkar

Abstract

Traffic congestion is a persistent problem in many metropolises worldwide. Despite the existence of traffic control systems, they are not always efficient enough to manage the ever-changing traffic density environment. The traditional approach of allocating specific times to each lane with the green light, regardless of the traffic situation, has not been very effective. In fact, it often can make the traffic congestion worse. Thus, the need for a more sophisticated system has emerged to simulate and optimize traffic control. This paper proposes the use of computer vision technology to develop a traffic control system that is based on periodic still photo feeds and compares different object detection models to find the best model for vehicle detection in our system . The system aims to enhance traffic flow by dynamically adjusting the traffic light cycles based on real-time traffic conditions.

Article Details

How to Cite
Phursule, R. ., Lal, D. ., Waghere, S. ., Mughni, M. A. ., Ransubhe, S. ., & Shiralkar, C. . (2023). Enhancing Traffic Flow Using Computer Vision Based - Dynamic Traffic Light Control and Lane Management. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 386–391. https://doi.org/10.17762/ijritcc.v11i7s.7014
Section
Articles

References

M. Maity, S. Banerjee and S. Sinha Chaudhuri, "Faster R-CNN and YOLO based Vehicle detection: A Survey," 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2021, pp. 1442-1447, doi: 10.1109/ICCMC51019.2021.9418274.

Gadde, S. ., & Chakravarthy, A. S. N. . (2023). Novel and Heuristic MolDoc Scoring Procedure for Identification of Staphylococcus Aureus. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 125 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2516

Cheng-Jian Lin, Shiou-Yun Jeng, Hong-Wei Lioa, "A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO", Mathematical Problems in Engineering, vol. 2021, Article ID 1577614, 10 pages, 2021. https://doi.org/10.1155/2021/1577614

CH. Ranga Reddy, Angshuman Roy, Ms. G. Priyanka, Dr. G. Victo Sudha George “VEHICLE DETECTION USING YOLO V3 FOR COUNTING THE VEHICLES AND TRAFFIC ANALYSIS” Dept. of Computer Science & Engineering, Dr. MGR Educational and Research Institute, Chennai, India , International Research Journal of Engineering and Technology (IRJET), 2022

Prof. Shweta Jain. (2017). Design and Analysis of Low Power Hybrid Braun Multiplier using Ladner Fischer Adder. International Journal of New Practices in Management and Engineering, 6(03), 07 - 12. https://doi.org/10.17762/ijnpme.v6i03.59

Prof. Pallavi Hiwarkar; Damini Bambal; Rishabh Roy. ”Vehicle Detection System & Counting Of Vehicles In Still Images Using Deep Learning”, International Journal of Creative Research Thoughts (IJCRT), 2022

Sultana, F., et al. “A Review of Object Detection Models Based on Convolutional Neural Network.” A Review of Object Detection Models Based on Convolutional Neural Network SpringerLink, 9 June 2020, doi:10.1007/978-981-15-4288-6_1.

Syed Sahil Abbas Zaidi, Mohammad Samar Ansari, Asra Aslam, Nadia Kanwal, Mamoona Asghar, and Brian Lee, “A Survey of Modern Deep Learning Based Object Detection Models.”, ScienceDirect, 8 Mar. 2022, doi:10.1016/j.dsp.2022.103514.

Thompson, A., Walker, A., Fernández, C., González, J., & Perez, A. Enhancing Engineering Decision Making with Machine Learning Algorithms. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/127

P. Adarsh, P. Rathi, and M. Kumar, "YOLO v3-Tiny: Object Detection and Recognition using one stage improved model" 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 687-694, doi: 10.1109/ICACCS48705.2020.9074315.

Srivastava, S., Divekar, A.V., Anilkumar, “Comparative analysis of deep learning image detection algorithms”. J Big Data 8, 66 (2021). https://doi.org/10.1186/s40537-021-00434-w

Shinde, Shubham & Kothari, Ashwin & Gupta, Vikram. (2018). YOLO based Human Action Recognition and Localization. Procedia Computer Science. 133. 831-838. 10.1016/j.procs.2018.07.112.