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<p>The uneven distribution of the temperature field in the track structure, caused by various meteorological factors such as extremely low temperatures and snowfall, leads to significant temperature loads and is the primary cause of damage to China Railway Track System (CRTS) III ballastless tracks in cold regions during service. In this study, to predict the temperature of the track structure accurately, we analyzed meteorological data collected from Shenyang, China, and identified the factors that had the most effect on the track temperature field. We propose a temporal convolutional network (TCN)-based temperature field prediction model for ballastless tracks (TCN-Track model), which enhances the ability to extract and fuse local and global features from complex long-term meteorological data. The results indicate that the proposed TCN-Track model performs well in predicting track temperature fields from meteorological data, with a mean absolute error (MAE) ranging from 0.26 to 0.39, a root mean square error (RMSE) ranging from 0.32 to 0.50, and correlation coefficient (<i>R</i>) values ranging from 0.888 to 0.985. Compared with a long short-term memory (LSTM) model, the MAE of the TCN-Track model is reduced by 89.17% and the RMSE by 88.51%. This method offers a new solution for accurately predicting the temperature field of ballastless tracks in cold regions, aiding in predicting and preventing track damage caused by low temperatures.</p>

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Predicting the temperature of CRTS III ballastless tracks in cold regions based on a TCN-Track model

  • 期刊论文

  • 发表日期：

    2025年11月29日

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  • Jie Liang,
  • Shijie Deng,
  • Juanjuan Ren,
  • Wenlong Ye,
  • Kaiyao Zhang,
  • Dacheng Li,
  • Ronghe Zhang

摘要

The uneven distribution of the temperature field in the track structure, caused by various meteorological factors such as extremely low temperatures and snowfall, leads to significant temperature loads and is the primary cause of damage to China Railway Track System (CRTS) III ballastless tracks in cold regions during service. In this study, to predict the temperature of the track structure accurately, we analyzed meteorological data collected from Shenyang, China, and identified the factors that had the most effect on the track temperature field. We propose a temporal convolutional network (TCN)-based temperature field prediction model for ballastless tracks (TCN-Track model), which enhances the ability to extract and fuse local and global features from complex long-term meteorological data. The results indicate that the proposed TCN-Track model performs well in predicting track temperature fields from meteorological data, with a mean absolute error (MAE) ranging from 0.26 to 0.39, a root mean square error (RMSE) ranging from 0.32 to 0.50, and correlation coefficient (R) values ranging from 0.888 to 0.985. Compared with a long short-term memory (LSTM) model, the MAE of the TCN-Track model is reduced by 89.17% and the RMSE by 88.51%. This method offers a new solution for accurately predicting the temperature field of ballastless tracks in cold regions, aiding in predicting and preventing track damage caused by low temperatures.