To Develop an Efficient Critical Vehicle Seamless Movement Technique using AI and ML Methods

Abstract

Abstract: Traffic congestion is becoming one of the critical issues by posing significant hurdles to critical vehicles, to nature by posing significant pollution. Not only can traffic congestion delay the movement of essential vehicles, but it can also extend travel times, contribute to driver stress, and increase fuel consumption and pollution. Megacities struggle the most from traffic congestion, despite the fact that they seem to be everywhere. Because of this, real-time road traffic density calculations are necessary for better signal control and traffic management. The traffic controller is one of the key elements influencing traffic flow. Therefore, in order to better fulfil this expanding demand, traffic control needs to be improved. Our system's objective is to use real-time traffic junction camera photos to compute traffic density using AI and image processing. It also focuses on the algorithm for changing traffic signals based on the movement of essential vehicles and vehicle density to reduce congestion, speed up the movement of essential vehicles and reduce pollution.