A vital function of the intelligent transportation system is traffic flow prediction. This is an exact forecast of the amount of traffic in a certain location on a given day in the future. The study of traffic forecasting helps to lessen traffic while promoting more affordable, safe, and efficient forms of transportation. Although conventional models rely on shallow networks, the number of vehicles has increased exponentially in recent years, making these standard machine learning methods unsuitable for the present situations. The proposed research presents a voting classifier‐based machine learning algorithm (ML) that combines many ML techniques, including random forest, naive Bayes, logistic regression, and SVM. The experimental findings demonstrate that, in comparison to the conventional methods, the suggested voting classifier achieved greater accuracy and precision rate.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Voting Classifier‐Based Machine Learning Technique for the Prediction of the Traffic Flow for the Intelligent Transportation System




    Publication date :

    2025-08-07


    Size :

    16 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Traffic Flow Prediction For Intelligent Transportation System Using Machine Learning

    Manikandan B.V. / Nathan T.R. / Naresh R. et al. | DOAJ | 2023

    Free access

    Traffic Prediction for Intelligent Transportation System Using Machine Learning

    Swathi, V. / Yerraboina, Sirisha / Mallikarjun, G. et al. | IEEE | 2022


    Traffic Flow Forecasting in Intelligent Transportation Systems Prediction Using Machine Learning

    Hossain, Mohammad Naveed / Ahmed, Nafim / Wazid Ullah, S. M. | IEEE | 2022



    Prediction of Water Potability Through Voting Classifier

    Kandula, Ashok Reddy / Sri, Kancharla Sowmya / Sumithra, Gudipudi et al. | IEEE | 2024