This paper presents a prototype design for a smart traffic system to be used at junction roads. This project uses traffic cameras for vehicle detection where automatically the signal timing changes by sensing the traffic data. Here, the parameters of the traffic dataset are the number of vehicles, speed, and vehicle type. If there is no vehicle on the road with a green signal, the traffic keeps accumulating on other roads of the junction. The conventional traffic system uses a fixed time, and its performance is considered poor in efficiency. To provide an alternative to conventional traffic systems, this proposed system is designed to effectively manage traffic congestion. This proposed method uses computer vision and analyzer algorithms to detect the vehicle’s speed and calculate whether or not the vehicle will be able to reach the stop line to pass through within the available green signal time in real-time. If there is no vehicle in the green zone of a traffic light that has the green light, then it will check the other camera which is planted at a particular distance away from the stop line to detect vehicles and if there is a vehicle crossing this camera, the computer vision and the analyzer will check the speed, of the vehicle and calculates whether the vehicle will pass the traffic light before the red light is ON. If the detected vehicle will not pass on time the traffic light will automatically change to Red and the next road will get a green light instantaneously. So, the road traffic can be cleared and the signal time can be used effectively, Thus it will address the problem which causes traffic congestion at the junctions and more importantly prioritize green signal changes for emergency vehicles in real-time scenarios.


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    Title :

    Smart Traffic Monitoring System


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:

    Conference:

    International Conference on Information Technology ; 2023 ; Noida, India March 02, 2023 - March 03, 2023



    Publication date :

    2023-12-15


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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