Delay prediction based on real-world train operation records is an essential issue to the delay management. In this paper, we present the first application of gradient boosting regression tress (GBRT) prediction model that can capture the relation between train delays and various characteristics of a railway system. Delayed train number (DN), station code (SC), scheduled time of arrival at a station (ST), time travelled (TT), distance travelled (DT), and percent of journey completed distance-wise (PC) are selected as the explanatory variables, and the delay time (WD) is the target variable. The model can evaluate various impact factors on train delays, which can assist dispatchers to make decisions. The results demonstrate that the GBRT model has a higher prediction precision and outperforms the support-vector machine (SVR) model and the random forest (RF) model.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Arrival Train Delays Prediction Based on Gradient Boosting Regression Tress


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liu, Baoming (editor) / Jia, Limin (editor) / Qin, Yong (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Shi, Rui (author) / Wang, Jing (author) / Xu, Xinyue (author) / Wang, Mingming (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2019 ; Qingdao, China October 25, 2019 - October 27, 2019



    Publication date :

    2020-04-02


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Arrival Train Delays Prediction Based on Gradient Boosting Regression Tress

    Shi, Rui / Wang, Jing / Xu, Xinyue et al. | British Library Conference Proceedings | 2020


    Arrival Train Delays Prediction Based on Gradient Boosting Regression Tress

    Shi, Rui / Wang, Jing / Xu, Xinyue et al. | TIBKAT | 2020



    Real-time bus arrival delays analysis using seemingly unrelated regression model

    Zhang, Qi / Ma, Zhenliang / Zhang, Pengfei et al. | Springer Verlag | 2024

    Free access

    Train Arrival Delay Prediction Based on a CNN-LSTM Approach

    Li, Jianmin / Xu, Xinyue / Zhao, Meng et al. | TIBKAT | 2021