Implementation of Machine learning in Smart Traffic Signal Control System with Operation of Open CV

  • Ms. Monali Gulhane, Dipali Choudhari, Priyanshi Dhoke, Samiksha Meshram, Dhanashree Nagpure
Keywords: Heavyload, No. of vehicles, Machine Learning, Q-learning, Time slot, Traffic signal optimization, adaptive signal synchronization.

Abstract

Traffic Management is an issue which impacts us almost daily. Use of technology and real time analysis can actually lead to a smooth traffic management. The common reason for traffic congestion is due to poor traffic prioritisation. While the number of vehicles are increasing at a fast pace, the infrastructure in the cities are not being able to match this growth. Our solution to this problem can be used for many urban cities where traffic jams during rush hours are becoming a routine affair, especially in the internal sectors where long queues of vehicles can be seen stranded. Therefore, we have tried to address the problem with the help of our project wherein the focus would be to minimise the vehicular congestion. We have achieved this with the help of image processing that can be obtained from surveillance cameras and eventually to deploy a feedback mechanism in the working of the traffic lights where the density of the traffic would also be factored in the decision-making process. Adaptive traffic signal control in a connected vehicle environment has shown a powerful ability to effectively alleviate urban traffic congestions to achieve desirable objectives. Future research is needed to develop more efficient and generic adaptive traffic signal control methods in a connected vehicle environment

Published
2021-07-07
How to Cite
Samiksha Meshram, Dhanashree Nagpure, M. M. G. D. C. P. D. (2021). Implementation of Machine learning in Smart Traffic Signal Control System with Operation of Open CV. Design Engineering, 1003-1011. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/2538
Section
Articles