TY - JOUR
T1 - A wind hazard warning system for safe and efficient operation of high-speed trains
AU - Gou, Hongye
AU - Chen, Xuanying
AU - Bao, Yi
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/12
Y1 - 2021/12
N2 - Operation safety and efficiency of high-speed trains are prone to strong winds. This study proposes a wind hazard warning system to predict wind speed and provide warning for safe and efficient operation of trains, and shows the implementation to the Lanzhou-Xinjiang High-Speed Railway. The system is composed of monitoring, remote and central processing, and execution modules. The system uses field monitoring data to predict wind speed and analyze responses of trains. Wind speed is predicted through integrating three models based on artificial neural networks and the predictions are incorporated into operation control for high speed trains under wind load. The results show that the proposed predictive model is capable of predicting wind speed, and the system is capable of providing reasonable warning for high-speed trains. It is anticipated that the system will improve the operation and management of high-speed trains for high safety and efficiency.
AB - Operation safety and efficiency of high-speed trains are prone to strong winds. This study proposes a wind hazard warning system to predict wind speed and provide warning for safe and efficient operation of trains, and shows the implementation to the Lanzhou-Xinjiang High-Speed Railway. The system is composed of monitoring, remote and central processing, and execution modules. The system uses field monitoring data to predict wind speed and analyze responses of trains. Wind speed is predicted through integrating three models based on artificial neural networks and the predictions are incorporated into operation control for high speed trains under wind load. The results show that the proposed predictive model is capable of predicting wind speed, and the system is capable of providing reasonable warning for high-speed trains. It is anticipated that the system will improve the operation and management of high-speed trains for high safety and efficiency.
KW - Ensembled empirical mode decomposition (EEMD)
KW - Extreme wind
KW - High-speed trains
KW - Machine learning
KW - Predictive model
KW - Wind speed
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U2 - 10.1016/j.autcon.2021.103952
DO - 10.1016/j.autcon.2021.103952
M3 - Article
AN - SCOPUS:85115757244
SN - 0926-5805
VL - 132
JO - Automation in Construction
JF - Automation in Construction
M1 - 103952
ER -