Time-Position Trajectory Prediction of Trains in Virtual Coupling Based on ATT-CNN-BiLSTM
In virtual coupling,predicting operation states of trains accurately is a central problem in ensuring the smooth tracking of trains.Considering the ever-changing characteristics of train operations,a spatio-temporal trajectory prediction method was proposed based on convolutional bidirectional long short-term memory neural network with atten-tion mechanism(ATT-CNN-BiLSTM).To address the problem of imbalanced data caused by few abnormal train opera-tion scenarios in historical train operation data,convolutional neural network and bi-directional long short-term memory network were used to extract feature correlations between dimensions of train operation data,with attention mechanism added to enhance generalization ability.Meanwhile,the runtime verification method was introduced to monitor the pre-diction results online to reduce the operational risks caused by prediction errors.Based on the data of Chengdu Metro Line 8 for experiment,the ATT-CNN-BiLSTM model proposed in this paper was evaluated by baseline model and abla-tion experiment with 5 evaluation indexes.The results show that the prediction error of the model for abnormal scenes is reduced by at least 9.626％.
train state predictionvirtual couplingdeep learningattention mechanismbi-directional LSTM
万方数据
维普
