Traffic congestion is a crucial issue that raises the uncertainty level of thetraveling duration resulting in high stress and unsafe traffic scenarios. Effective trafficestimation and forecasting via Intelligent Transportation Systems (ITS) applications arebeneficial in a variety of applications. The process of accurately and rapidly predictingthe traffic condition helps the travelers to determine the traveling path and makedecisions wisely. This paper develops a new deep learning (DL) based traffic densityestimation and prediction model for ITS. The proposed model involves a set of two DLmodels, namely convolutional neural network (CNN) and long short term memory(LSTM) for traffic density estimation and prediction. These models are applied, and theresults are analyzed under diverse situations. The experimental outcome indicated thatthe LSTM model is superior to CNN on both estimation and prediction processes.


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    Title :

    An Intelligent Transportation System for Traffic Density Estimation and Prediction Using Deep Learning Models


    Contributors:


    Publication date :

    2021



    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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