The increasing population in urban areas has led to an increase in the number of vehicles. The growth of vehicles is not comparable with the growth of roads that caused traffic congestion. To solve the congestion problem, traffic conditions are predicted. The method used to predict traffic congestion is Support Vector Regression (SVR). The map-Matching algorithm is used to adjust vehicle position data that is not accurate, resulting in an accurate position. In this research, the performance of SVR was tested for congestion prediction. The test was conducted to determine the performance of the SVR model by calculating the Means Absolute Error (MAE) and Root Mean Squared Error (RMSE). K-fold cross-validation is used to show the accuracy of the SVR model on the dataset after being divided into k sections and FCE to determine the level of congestion. We conclude that the SVR has good performance for congestion prediction, this is indicated by a small error value and high accuracy value. The prediction results using SVR are the same as the actual condition.


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    Titel :

    Analysis Performance of Support Vector Regression (SVR) for Congestion Prediction


    Beteiligte:


    Erscheinungsdatum :

    16.11.2022


    Format / Umfang :

    338087 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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