Abstract The predication of short-term passenger flow plays a very important role for improving service quality and revenue High-speed railway operation. To precisely predict the short-term passenger flow, impact factors need to be deeply analyzed and a reasonable predication model is required. This chapter analyzed the impact factors for short-term passenger flow and proposed a prediction model based on random forest regression. With the passenger flow data between Beijing and Shanghai from July to August in 2015, a predication model is trained and reached 91% accuracy for daily passenger flow. Finally, the importance of each impact factor has been analyzed, and this information can also help high-speed railway operation. It is shown that the prediction model based on random forest regression for predicting short-term passenger flow can help to improve the high-speed railway operation.


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    Titel :

    Prediction Modeling of Railway Short-Term Passenger Flow Based on Random Forest Regression


    Beteiligte:
    Li, Li-hui (Autor:in) / Zhu, Jian-sheng (Autor:in) / Shan, Xing-hua (Autor:in) / Zhang, Xia (Autor:in)


    Erscheinungsdatum :

    2018-09-16


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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