Volume 24 Issue 3
Jun.  2024
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2024  >  24(3): 296-310
ZHANG Miao, HE Yi-juan, YANG Bo-yu, LUO Zheng-wei, LU Wan-li, TANG Tao, LI Kai-cheng, LYU Ji-dong. Trajectory prediction method for high-speed train group tracking operation based on LSTM-KF model[J]. Journal of Traffic and Transportation Engineering, 2024, 24(3): 296-310. doi: 10.19818/j.cnki.1671-1637.2024.03.021
Citation: ZHANG Miao, HE Yi-juan, YANG Bo-yu, LUO Zheng-wei, LU Wan-li, TANG Tao, LI Kai-cheng, LYU Ji-dong. Trajectory prediction method for high-speed train group tracking operation based on LSTM-KF model[J]. Journal of Traffic and Transportation Engineering, 2024, 24(3): 296-310. doi: 10.19818/j.cnki.1671-1637.2024.03.021
shu

Trajectory prediction method for high-speed train group tracking operation based on LSTM-KF model

doi: 10.19818/j.cnki.1671-1637.2024.03.021
  • 1.

    Center of National Railway Intelligent Transportation System Engineering and Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China

  • 2.

    National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China

  • 3.

    Institute of Computer Science, Humboldt University of Berlin, Berlin BE 10489, Germany

  • 4.

    State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, China

Funds:

Science and Technology Research and Development Project of China State Railway Co., Ltd. L2021G003

National Natural Science Foundation of China 52272329

Natural Science Foundation of Beijing L211019

More Information
  • Author Bio:

    ZHANG Miao(1987-), male, associate professor, PhD, 13810162613@163.com

    LYU Ji-dong(1981-), male, professor, PhD, jdlv@bjtu.edu.cn

  • Received Date: 2024-01-20
    Available Online: 2024-07-18
  • Publish Date: 2024-06-30
    Abstract
  • The problem of operation trajectory prediction in high-speed train group tracking was studied to further shorten train tracking distance and improve transportation capacity. The advantages of the long short-term memory (LSTM) model in processing sequence data and the ability of the Kalman filter (KF) model to process noise were considered, and a new LSTM-KF model for train trajectory prediction was proposed. The historical data of train operation were used for LSTM model training, and train trajectory prediction curve was generated. The predicted results and dynamics mechanism were integrated by the KF model to correct the calculation results, smoothing the train trajectory predicted by the LSTM model. The experiment was simulated and verified based on the standard data of lines on the simulation and test platform of the high-speed railway train control system. Simulation results show that the prediction errors of the position relative to the true value of the three models of LSTM-KF, LSTM, and recurrent neural networks (RNN) are 78, 798, and 911 m, respectively after 30 predicted steps under the cruise driving condition. The prediction errors of the speed are 1, 22, and 1 m·s-1, respectively. The position root mean square error (RMSE) of the LSTM-KF is 7% and 15% of that of LSTM and RNN, and the speed RMSE of the LSTM-KF model is 14% and 30% of that of LSTM and RNN. The mean position prediction errors under acceleration condition are 94, 294, and 2 691 m, respectively, and the mean speed prediction errors are 0.09, 10.05, and 2.74 m·s-1, respectively. The mean position prediction errors under deceleration condition are 1 181, 4 135, and 4 079 m, respectively, and the mean speed prediction errors are 1.14, 6.01, and 13.52 m·s-1, respectively. It can be seen that the prediction accuracy under different operating conditions can be significantly improved in the LSTM-KF model, and long-term data sequences can be effectively generated to provide decision-making for the tracking operations of high-speed train groups.

     

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