This paper proposes a data-driven method that combines eXtreme Gradient Boosting (XGBoost) and Bayesian optimization algorithm to predict train arrival delays. First, 11 characteristics that may affect train arrival delay at the next station are identified as independent variables. Second, an XGBoost prediction model that capture the relation between train arrival delays and various characteristics of a railway system is established. Third, the Bayesian optimization algorithm is applied to the hyper-parameter optimization of XGBoost model to improve the prediction accuracy. Finally, a case study is illustrated to show the prediction accuracy of the proposed method. The results demonstrate that the proposed method has a higher prediction precision and outperforms other benchmark methods (i.e., Random Forest, Deep Extreme Learning machine and Gradient Boosting Regression Tress).


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    Titel :

    A Train Arrival Delay Prediction model using XGBoost and Bayesian Optimization*


    Beteiligte:
    Shi, Rui (Autor:in) / Xu, Xinyue (Autor:in)


    Erscheinungsdatum :

    2020-09-20


    Format / Umfang :

    448420 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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