Being on time is getting more difficult these days for a multitude of reasons, including transportation congestion. As is generally known, there are four lanes, each with a traffic signal. There are times when a high-density lane has a red light but the lane across from it has a green signal. This leads us to the question of high-density road lane time. We create a solution to this problem by tracking moving cars on the road using a camera and controlling time based on their motions. Our traffic management strategy includes the deployment of a unique intelligence that employs real-time image processing to determine traffic density based on data from webcams or IP cameras located at traffic junctions. It also focuses on the algorithm for modifying traffic lights depending on vehicle density on the road in order to minimize traffic congestion and the number of accidents. As a result, it will save fuel consumption and waiting times while providing safe transportation for passengers. It will also provide a significant quantity of data to benefit future road research and planning efforts. In subsequent stages, the aim of reduced traffic congestion and uninterrupted traffic flow may be realized by synchronizing many traffic lights with one another. Instead than using electrical sensors installed on the street, the system detects the automobile using photos. There will be a camera at the traffic light. The option to capture photographs consecutively will be useful. Image processing is a more effective approach for handling traffic light state changes. Because it uses actual traffic images, it illustrates that it can minimize traffic congestion and save time wasted waiting at a green light with an empty vehicle.


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    Title :

    Smart Traffic Signal Using Real-Time Vehicle Tracking System


    Additional title:

    Lect. Notes in Networks, Syst.


    Contributors:

    Conference:

    International Conference on Data-Processing and Networking ; 2024 ; České Budějovice, Czech Republic October 25, 2024 - October 26, 2024



    Publication date :

    2025-07-05


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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