By using vehicle-to-vehicle (V2V) communication technology to interconnect trains while maintain a shorten distance under the premise of safety condition, is the development direction for improving the efficiency of high-speed trains. Trajectory prediction of train ahead is an important mean to further reduce the tracking distance. In this paper, based on Gaussian mixture model (GMM) and long short-term memory (LSTM) Recurrent Neural Network (RNN), we propose a personalized trajectory prediction method model for high-speed trains. The main idea is to achieve accurate and personalized trajectory prediction by recognizing the driving style of the train ahead to realize a shorter distance tracking control. Firstly, based on the GMM, three different driving styles are identified by combining the characteristic data of tracking trains, and the characteristic importance of driving styles are analyzed by MIC. Secondly, based on different driving styles, a novelty personalized trajectory prediction algorithm is worked out by modified LSTM-RNN models. Finally, experiments are carried out using the real data of the on-board equipment and ground control equipment. The results indicate that, compared with the traditional trajectory prediction methods, the proposed personalized trajectory prediction method shows significant advantages.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Trajectory Prediction of High-Speed Train Based on GMM-LSTM


    Beteiligte:
    Tian, Wanqi (Autor:in) / Bu, Bing (Autor:in) / Lv, Jidong (Autor:in) / Tang, Tao (Autor:in) / Li, Kaicheng (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    2887076 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Trajectory Prediction of High-Speed Train Group Tracking Based on LSTM-KF Hybrid Model

    Yang, Boyu / Lv, Jidong / Liu, Hongjie et al. | IEEE | 2024


    Pedstrian Trajectory Prediction Based on LSTM

    Li, Shaosong / Jiang, Junchen / Zhou, Qingbin et al. | IEEE | 2023


    Speed trajectory optimization for a high-speed train

    Xiao, Zhuang / Zhao, Zining / Wang, Qingyuan et al. | TIBKAT | 2020


    Research on Fault Prediction of High-Speed Train Auxiliary Power Supply System Based on LSTM

    Wang, Zhuo / Dong, Honghui / Man, Jie et al. | Springer Verlag | 2022


    Space-time attention LSTM vehicle trajectory prediction method based on position-speed

    SONG XIULAN / YANG LUSHENG / DONG ZHAOHANG et al. | Europäisches Patentamt | 2023

    Freier Zugriff