Data-driven models for train control dynamics in high-speed railways: LAG-LSTM for train trajectory prediction

• Published in: Information sciences 2022-07, Vol.600, p.377-400
• Main Authors: Yin, Jiateng, Ning, Chenhe, Tang, Tao
• Format: Article
• Language: English
• Subjects: Data-driven; High-speed train; LAG-LSTM; Time delay; Train control model; Trajectory prediction
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2022.04.004

•Construct a novel LAG-LSTM (long short-term memory with lagged information) model.•Develop three modules, i.e., a time-delay variable module (TDVM), a state variable enhancement module (SVEM), and a Pre-LSTM module.•Real-world case studies demonstrate the effectiveness of LAG-LSTM in HSR. The construction of an accurate train control model (TCM) is crucial to the design of automatic train operation and real-time traffic management systems in high-speed railways. Traditional physical-driven models usually fail to reflect the “true” dynamics of high-speed trains (HSTs) because of the strong nonlinearity and uncertainty due to air resistance, frequently switching working conditions, and variations in external influencing factors such as weather and temperature. Although some data-driven deep learning models have recently been proposed for environmental adaptation, they are all “black-box” models, which cannot explain how the input of the models affects the HST output. To overcome these issues, this study constructs a novel long short-term memory with lagged information (LAG-LSTM) model by combining the physical-driven HST model and an “interpretable” deep learning model. Specifically, our LAG-LSTM model contains three modules: a time-delay variable module to model the transform delay of control variables, state variable enhancement module to extract the key features among high-dimensional input data, and Pre-LSTM module to predict the future train trajectory with given control variables. We collected field data from the Beijing-Shanghai high-speed railway and developed a data filter method and a normalization procedure to overcome the positioning errors of HSTs and construct a standard data set. Finally, we tested the effectiveness of our LAG-LSTM by comparing it with six deep learning structures, including fully connected neural network, recursive neural network, standard LSTM, and LSTM with convolutional layers. The results show that LAG-LSTM can accurately predict the trajectories of HSTs and outperforms other deep learning models. Regarding prediction accuracy, LAG-LSTM improved the performance of the traditional LSTM by 13.5% to 23.3%.