With the continuous increase in the number of vehicles, severe traffic jams have become an increasingly common problem, which will directly affect the time consumed and money spent of the transport users. Predicting future traffic flow can help alleviate this problem to a certain extent. In this work, we firstly preprocess the data via selenium, OSS, and Message Queue and model the traffic flow using three machine learning algorithms, including Linear regression, Decision Tree, and Support Vector Machine. Then, we analyze the actual traffic data of Beijing on the Baidu map. This work shows that the accuracy of Random Forest is 0.719, which is the highest in these three methods. And the second is Logistic Regression and SVM.


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    Titel :

    Traffic Flow Prediction Using Machine Learning Methods


    Beteiligte:
    Wang, Hainan (Autor:in) / Wei, Xuetong (Autor:in) / Yao, Junyuan (Autor:in) / Zhang, Yue (Autor:in)


    Erscheinungsdatum :

    01.12.2021


    Format / Umfang :

    3973730 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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