To enhance decision-making and encourage urban development in VANET, a subclass of mobile ad hoc network, the suggested approach elucidates how to enhance interconnection in VANET in order to prevent and identify traffic congestion. The solution is constructed on an algorithm grounded on machine learning. The recommended algorithm is called YOLO (You Only Look Once) algorithm. This proposed method improves the connectivity of the GPS and several VANET network devices. Reducing the effects of traffic congestion besides traffic accidents considering those two as external aspects of transportation, is one of the main goals of transportation policy makers. Road traffic accidents have also had a substantial economic impact on people, property, and society at large. It is crucial to lessen the effects of accidents and traffic congestion because both burden societies. Augmented travel time caused through congestion of traffic enforces unrestricted value towards road users which leads to the reduced quality of life and mobility. Owing to adjacent spacing of vehicles and overheating of vehicles, congestion in traffic increases the risk of traffic accidents. YOLO (You only look once) implemented technology is used in this proposed system to predict automobile traffic congestion. Machine learning models for predicting traffic congestion is developed. In order to anticipate future traffic congestion, a machine learning system can be trained using the data. Decisions concerning routes, speeds, and other traffic control tactics can then be made using the trained model. Machine learning can also be incorporated to find data patterns which can be processed further to increase the precision of traffic predictions.


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    Title :

    Machine Learning Based Traffic Congestion and Accident Prevention Analysis


    Additional title:

    Communic.Comp.Inf.Science



    Conference:

    International Conference on Advances in Artificial Intelligence and Machine Learning in Big Data Processinging ; 2023 ; Chennai, India August 16, 2023 - August 17, 2023



    Publication date :

    2024-10-01


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English