At present, vehicular traffic flow prediction is treated as a crucial issue in theintelligent transportation system. It mainly focuses on the estimation of vehiculartraffic flow on roadways or stations in the subsequent time interval ahead of the future.Generally, traffic flow prediction comprises two major stages, namely feature learningand predictive modeling. In this view, this paper introduces an Intelligent VehicularTraffic Flow Prediction (IVTFP) model to effectively predict the flow of traffic on theroad. The proposed IVTFP model involves two main stages, namely feature selection(FS) and classification. At the first level, the whale optimization algorithm (WOA) isapplied as a feature selector called WOA-FS to select the useful subset of features.Next, in the second level, the multiple linear regression (MLR) technique is utilized asa prediction model to forecast the traffic flow. The performance of the IVTFP modeltakes place on the benchmark Brazil dataset. The simulation outcome indicated theeffective outcome of the IVTFP model, and it ensured that the application of theWOA-FS model helps attain improved classification outcomes.


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    Titel :

    An Intelligent Vehicular Traffic Flow Prediction Model Using Whale Optimization with Multiple Linear Regression


    Beteiligte:
    Gogineni, Hima Bindu (Autor:in) / Lydia, E. Laxmi (Autor:in) / Supriya, N. (Autor:in)


    Erscheinungsdatum :

    2021



    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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