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基于LSTM-KF模型的高速列车群组追踪运行轨迹预测方法

张淼 何仪娟 杨博宇 罗正伟 卢万里 唐涛 李开成 吕继东

张淼, 何仪娟, 杨博宇, 罗正伟, 卢万里, 唐涛, 李开成, 吕继东. 基于LSTM-KF模型的高速列车群组追踪运行轨迹预测方法[J]. 交通运输工程学报, 2024, 24(3): 296-310. doi: 10.19818/j.cnki.1671-1637.2024.03.021
引用本文: 张淼, 何仪娟, 杨博宇, 罗正伟, 卢万里, 唐涛, 李开成, 吕继东. 基于LSTM-KF模型的高速列车群组追踪运行轨迹预测方法[J]. 交通运输工程学报, 2024, 24(3): 296-310. doi: 10.19818/j.cnki.1671-1637.2024.03.021
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

基于LSTM-KF模型的高速列车群组追踪运行轨迹预测方法

doi: 10.19818/j.cnki.1671-1637.2024.03.021
基金项目: 

中国国家铁路集团有限公司科技研究开发计划 L2021G003

国家自然科学基金项目 52272329

北京市自然科学基金项目 L211019

详细信息
    作者简介:

    张淼(1987-), 男, 陕西佳县人, 中国铁道科学研究院集团有限公司副研究员, 工学博士, 从事轨道交通列车运行控制研究

    通讯作者:

    吕继东(1981-), 男, 河北廊坊人, 北京交通大学教授, 工学博士

  • 中图分类号: U283.1

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

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
Article Text (Baidu Translation)
  • 摘要: 为进一步缩小列车追踪距离以提高运力,研究了高速列车群组追踪运行轨迹预测问题;考虑长短期记忆网络(LSTM)模型处理序列数据的优势和卡尔曼滤波(KF)模型噪声处理的能力,提出了一种新型列车轨迹预测LSTM-KF模型;使用列车运行的历史数据进行LSTM模型训练,生成了列车轨迹预测曲线;KF模型结合预测结果和动力学机理,更正了计算结果,使LSTM模型预测的列车轨迹变得平滑;依托于高铁列控系统仿真测试平台的标准线路数据进行了仿真验证。仿真结果表明:在巡航工况下,30个预测步长后,LSTM-KF、LSTM和循环神经网络(RNN)模型的位置预测误差分别为78、798和911 m,速度相对真实值的预测误差分别为1、22和1 m·s-1,LSTM-KF模型的位置均方根误差(RMSE)分别为LSTM和RNN的7%和15%,LSTM-KF模型的速度RMSE分别为LSTM和RNN的14%和30%;在加速工况下,3个模型的位置预测误差均值分别为94、294和2 691 m,速度预测误差均值分别为0.09、10.05和2.74 m·s-1;在减速工况下,3个模型的位置预测误差均值分别为1 181、4 135和4 079 m,速度预测误差均值分别为1.14、6.01和13.52 m·s-1。可见,LSTM-KF模型在不同运行工况下均能显著提升预测精度,能够有效生成长时域数据序列,为高速列车群组追踪运行提供决策。

     

  • 图  1  高速列车传统运行与群组运行方式

    Figure  1.  Traditional and group operation modes of high-speed train

    图  2  LSTM模型内存块

    Figure  2.  LSTM model memory block

    图  3  基于LSTM-KF模型的列车轨迹预测原理

    Figure  3.  Train track prediction principle based on LSTM-KF model

    图  4  LSTM-KF模型框架

    Figure  4.  LSTM-KF model framework

    图  5  基于LSTM-KF模型的算法流程

    Figure  5.  Algorithm flow of LSTM-KF model

    图  6  高速铁路列车群组追踪运行场景

    Figure  6.  High-speed railway train group tracking operation scenario

    图  7  速度预测评价指标

    Figure  7.  Evaluation indexes of speed prediction

    图  8  位置预测评价指标

    Figure  8.  Evaluation indexes of position prediction

    图  9  巡航工况下不同模型的速度预测结果

    Figure  9.  Speed prediction results of different models under cruising conditions

    图  10  巡航工况下不同模型的位置预测结果

    Figure  10.  Position prediction results of different models under cruising conditions

    图  11  巡航工况下不同模型的加速度预测结果

    Figure  11.  Acceleration prediction results of different models under cruising conditions

    图  12  巡航工况下不同模型的两车间距预测结果

    Figure  12.  Prediction results of distances between two trains under cruising conditions using different models

    图  13  巡航工况下不同模型的两车速度差预测结果

    Figure  13.  Prediction results of speed differences between two trains under cruising conditions using different models

    图  14  加速工况下不同模型的位置预测结果

    Figure  14.  Position prediction results of different models under acceleration conditions

    图  15  加速工况下不同模型的速度预测结果

    Figure  15.  Speed prediction results of different models under acceleration conditions

    图  16  减速工况下不同模型的位置预测结果

    Figure  16.  Position prediction results of different models under deceleration conditions

    图  17  减速工况下不同模型的速度预测结果

    Figure  17.  Speed prediction results of different models under deceleration conditions

    表  1  试验对比参数设置

    Table  1.   Experimental comparison parameter settings

    参数 取值
    LSTM网络结构 100×100
    预测时窗/s 6
    历史时间步长 12
    未来时间步长 30
    状态迁移矩阵 $\left[\begin{array}{cc}0 & 0.5 \\ 0 & 1\end{array}\right]$
    观测矩阵 $\left[\begin{array}{cc}1 & 0 \\ 0 & 1\end{array}\right]$
    观测协方差矩阵 $\left[\begin{array}{cc}10 & 0 \\ 0 & 10\end{array}\right]$
    过程误差协方差矩阵 $\left[\begin{array}{cc}0.1 & 0 \\ 0 & 0.1\end{array}\right]$
    下载: 导出CSV

    表  2  巡航工况下不同模型的位置预测结果评价

    Table  2.   Evaluation of position prediction results of different models under cruising conditions

    时间步 RMSE/m MAE/m MAPE/%
    RNN LSTM-KF LSTM RNN LSTM-KF LSTM RNN LSTM-KF LSTM
    1 1 456.41 78.95 2 799.74 1 456.41 78.95 2 799.74 0.08 0.00 0.14
    5 1 591.03 97.88 2 464.24 1 586.20 96.49 2 453.65 0.08 0.01 0.12
    10 1 904.61 132.90 2 234.40 1 870.70 127.31 2 211.47 0.10 0.01 0.11
    15 2 431.12 163.92 2 083.96 2 311.75 155.26 2 053.49 0.12 0.01 0.11
    20 3 345.82 185.87 1 961.03 3 011.15 176.17 1 921.79 0.14 0.01 0.10
    25 4 966.02 196.82 1 850.34 4 144.52 187.91 1 799.06 0.18 0.01 0.09
    下载: 导出CSV

    表  3  巡航工况下不同模型的速度预测结果评价

    Table  3.   Evaluation of speed prediction results of different models under cruising conditions

    时间步 RMSE/(m·s-1) MAE/(m·s-1) MAPE/%
    RNN LSTM-KF LSTM RNN LSTM-KF LSTM RNN LSTM-KF LSTM
    1 1.02 1.58 22.07 1.02 1.58 22.07 0.01 0.02 0.19
    5 1.16 0.85 19.28 1.15 0.77 19.17 0.01 0.01 0.16
    10 1.40 0.65 16.40 1.37 0.53 15.87 0.01 0.01 0.14
    15 1.76 0.55 14.08 1.68 0.40 12.93 0.02 0.00 0.12
    20 2.34 0.51 12.40 2.13 0.39 10.64 0.02 0.00 0.10
    25 3.31 0.51 11.16 2.83 0.42 8.87 0.03 0.00 0.08
    下载: 导出CSV

    表  4  不同工况下真实值和LSTM-KF模型预测值

    Table  4.   Real values and predicted values of LSTM-KF model under different conditions

    时间步 加速工况-位置/m 加速工况-速度/(m·s-1) 减速工况-位置/m 减速工况-速度/(m·s-1)
    真实值 LSTM-KF模型预测值 真实值 LSTM-KF模型预测值 真实值 LSTM-KF模型预测值 真实值 LSTM-KF模型预测值
    1 4 806.00 4 892.41 70.56 74.69 18 499.00 18 894.95 96.67 96.88
    2 4 806.00 4 892.41 71.11 74.89 18 519.00 18 983.17 96.11 96.03
    3 4 849.00 4 936.37 71.39 74.89 18 499.00 19 082.20 95.83 95.25
    4 4 877.00 4 964.85 71.67 74.87 18 519.00 19 189.04 95.00 94.16
    5 4 949.00 5 037.90 71.94 74.82 18 499.00 19 300.63 94.72 93.36
    6 4 992.00 5 081.46 72.50 74.97 18 517.00 19 414.51 94.44 92.60
    7 5 021.00 5 111.03 72.78 74.90 18 499.00 19 529.68 93.89 91.75
    8 5 065.00 5 155.61 73.06 74.84 18 517.00 19 644.79 93.33 90.96
    9 5 094.00 5 185.20 73.61 74.95 18 498.00 19 759.37 93.06 90.37
    10 5 138.00 5 229.78 73.89 74.89 18 517.00 19 872.41 92.50 89.69
    11 5 168.00 5 260.35 74.17 74.83 18 497.00 19 983.59 92.22 89.22
    12 5 212.00 5 304.92 74.72 74.98 18 517.00 20 092.02 91.39 88.53
    13 5 242.00 5 335.48 75.00 74.95 18 497.00 20 197.32 91.11 88.16
    14 5 287.00 5 381.00 75.28 74.99 18 515.00 20 294.13 90.56 87.74
    15 5 317.00 5 411.49 75.56 75.04 18 498.00 20 388.78 90.28 87.49
    16 5 363.00 5 457.98 76.11 75.30 18 515.00 20 480.60 89.72 87.10
    17 5 393.00 5 488.45 76.39 75.39 18 496.00 20 569.20 89.17 86.74
    18 5 439.00 5 534.91 76.94 75.69 18 515.00 20 654.33 88.89 86.53
    19 5 470.00 5 566.35 77.22 75.82 18 496.00 20 735.88 88.61 86.33
    20 5 516.00 5 612.77 77.50 75.98 18 514.00 20 813.79 87.78 85.86
    21 5 547.00 5 644.17 77.78 76.15 18 496.00 20 888.06 87.50 85.68
    22 5 594.00 5 691.55 78.33 76.54 18 513.00 20 958.73 86.94 85.37
    23 5 625.00 5 722.92 78.61 76.77 18 496.00 21 025.86 86.67 85.20
    24 5 672.00 5 770.26 78.89 77.03 18 512.00 21 089.52 86.11 84.90
    25 5 704.00 5 802.59 79.44 77.50 18 496.00 21 149.84 85.56 84.60
    26 5 751.00 5 849.89 79.72 77.81 18 512.00 21 206.93 85.00 84.31
    27 5 783.00 5 882.19 80.00 78.16 18 495.00 21 260.98 84.72 84.15
    28 5 832.00 5 931.46 80.56 78.74 18 511.00 21 312.05 84.17 83.86
    29 5 864.00 5 963.72 80.83 79.16 18 495.00 21 360.27 83.89 83.71
    30 5 912.00 6 011.96 81.11 79.64 18 511.00 21 405.73 83.33 83.42
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-01-20
  • 网络出版日期:  2024-07-18
  • 刊出日期:  2024-06-30

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