With day to day computing becoming difficult. Lot of time and energy is lost in traversing through vehicular traffic. In this paper we propose a solution to predict vehicular traffic using machine learning techniques. US traffic 2015 dataset contains daily volumes of traffic, since this is an actual data and not a simulated one, patterns found in this research can be benefited and made valuable with differing datasets over time. Data driven solutions are proven to breakthrough tough problem statements. Thus in this paper we propose stage by stage machine learning processes to build an efficient model capable of predicting traffic volume based on features which brings out hidden insights in vehicular movements. Inspired by existing research exploration over traffic, in this paper we share our contribution of making traffic predictable thus saving time and energy in day to day life.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicular Traffic analysis and prediction using Machine learning algorithms


    Contributors:
    Moses, Andrew (author) / R, Parvathi (author)


    Publication date :

    2020-02-01


    Size :

    1018182 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Prediction of Traffic Flow Propagation Using Machine Learning Algorithms

    Priya, Shristi / Singh, Divyashu / Sharma, Harshit et al. | IEEE | 2022



    LTE Connectivity and Vehicular Traffic Prediction Based on Machine Learning Approaches

    Ide, Christoph / Hadiji, Fabian / Habel, Lars et al. | IEEE | 2015



    Machine Learning Solutions to Vehicular Traffic Congestion

    Chhatpar, Pavan / Doolani, Nimesh / Shahani, Sumeet et al. | IEEE | 2018