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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    Title :

    Traffic Prediction Using Machine Learning


    Additional title:

    Lecture Notes on Data Engineering and Communications Technologies


    Contributors:


    Publication date :

    2022-03-22


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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