Traffic Management and Signal Manipulation system using Machine Learning

This study explores a novel method to traffic control that enhances signal control systems in metropolitan environments by applying machine learning techniques. This research revolves around a methodology that improves vehicle flow and reduces traffic congestion. This paper elaborates on the methodology utilized for collection of data and vehicle detection methods used for optimizing traffic signals. The findings reveal noteworthy enhancements in improving vehicle detection such a as map50 of 80.5% of various types of vehicles pertaining to Indian traffic using Yolo V8. In essence, this research endeavors to transcend traditional paradigms of traffic management, ushering in a new era of intelligent infrastructure capable of harmonizing vehicular movement.