The paper deals with traffic prediction that can be done in intelligent transportation systems which involve the prediction between the previous year’s dataset and the recent year’s data which ultimately provides the accuracy and mean square error. This prediction will be helpful for the people who are in need to check the immediate traffic state. The traffic data is predicated on a basis of 1 h time gap. Live statistics of the traffic is analyzed from this prediction. So this will be easier to analyze when the user is on driving too. The system compares the data of all roads and determines the most populated roads of the city. I propose the regression model in order to predict the traffic using machine learning by importing Sklearn, Keras, and TensorFlow libraries.


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    Titel :

    Traffic Prediction Using Machine Learning


    Weitere Titelangaben:

    Lecture Notes on Data Engineering and Communications Technologies


    Beteiligte:
    Suma, V. (Herausgeber:in) / Fernando, Xavier (Herausgeber:in) / Du, Ke-Lin (Herausgeber:in) / Wang, Haoxiang (Herausgeber:in) / Deekshetha, H. R. (Autor:in) / Shreyas Madhav, A. V. (Autor:in) / Tyagi, Amit Kumar (Autor:in)


    Erscheinungsdatum :

    22.03.2022


    Format / Umfang :

    15 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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