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2691 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1181、4135和4079 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。
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.4 tabs,17 figs,30 refs.
作者
张淼
何仪娟
杨博宇
罗正伟
卢万里
唐涛
李开成
吕继东
ZHANG Miao;HE Yi-juan;YANG Bo-yu;LUO Zheng-wei;LU Wan-li;TANG Tao;LI Kai-cheng;LYU Ji-dong(Center of National Railway Intelligent Transportation System Engineering and Technology,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;National Engineering Research Center of Rail Transportation Operation and Control System,Beijing Jiaotong University,Beijing 100044,China;Institute of Computer Science,Humboldt University of Berlin,Berlin BE 10489,Germany;State Key Laboratory of Advanced Rail Autonomous Operation,Beijing Jiaotong University,Beijing 100044,China)
出处
《交通运输工程学报》
EI
CSCD
北大核心
2024年第3期296-310,共15页
Journal of Traffic and Transportation Engineering
基金
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)。
关键词
高速列车
群组追踪运行
车-车通信
轨迹预测
长短期记忆网络
卡尔曼滤波
high-speed train
group tracking operation
train to train communication
trajectory prediction
long short-term memory network
Kalman filter
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。
