JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A

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

Author(s): Jie LIANG^1, ^2, ⁴, Shijie DENG^1, ², Juanjuan REN^1, ², Wenlong YE^1, ^2, ⁴, Kaiyao ZHANG⁵, Dacheng LI³, Ronghe ZHANG³
Affiliation(s): ¹School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China; more
Corresponding email(s): dkjust@my.swjtu.edu.cn
Key Words: Cold regions; CRTS III ballastless tracks; Temperature prediction; Meteorological variables; Time prediction model.

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Jie LIANG^1,^2,⁴, Shijie DENG^1,², Juanjuan REN^1,², Wenlong YE^1,^2,⁴, Kaiyao ZHANG⁵, Dacheng LI³, Ronghe ZHANG³. Predicting the temperature of CRTS III ballastless tracks in cold regions based on a TCN-Track model[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2400527

@article{title="Predicting the temperature of CRTS III ballastless tracks in cold regions based on a TCN-Track model",
author="Jie LIANG^1,^2,⁴, Shijie DENG^1,², Juanjuan REN^1,², Wenlong YE^1,^2,⁴, Kaiyao ZHANG⁵, Dacheng LI³, Ronghe ZHANG³",
journal="Journal of Zhejiang University Science A",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.A2400527"
}

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%A Shijie DENG^1
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%A Juanjuan REN^1
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%A Dacheng LI³
%A Ronghe ZHANG³
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doi="https://doi.org/10.1631/jzus.A2400527"

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T1 - Predicting the temperature of CRTS III ballastless tracks in cold regions based on a TCN-Track model
A1 - Jie LIANG^{1
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A1 - Juanjuan REN^{1
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A1 - Wenlong YE^{1
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A1 - Kaiyao ZHANG⁵
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A1 - Ronghe ZHANG³
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doi="https://doi.org/10.1631/jzus.A2400527"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: 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 the Shenyang area and identified the factors that had most effect on the track temperature field. We propose a Temporal Convolutional Network-based (TCN-based) temperature field prediction model for ballastless tracks (TCN-Track), 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 was 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.

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