Prediction of CO concentration in extra-long tunnels based on time series

Abstract

Abstract: The time series analysis method has the advantage of modeling and predicting the time series data, which can more accurately reveal the law and trend of CO concentration in tunnels. By collecting the historical data of CO concentration, vehicle flow, vehicle speed and wind speed in Liupanshan tunnel for three consecutive years, a prediction model of CO concentration was constructed by time series analysis method. At the same time, the factors affecting the change of CO concentration (vehicle flow, vehicle speed, wind speed) were analyzed. The results show that the increase of vehicle flow, the decrease of vehicle speed and the lower wind speed may lead to the increase of CO concentration. The average difference between the predicted value of the prediction model and the measured value is small, indicating that the prediction model has certain accuracy and prediction ability. The application of the prediction model can provide a scientific basis for traffic management and environmental protection, reduce potential safety risks, and provide early warning and decision-making for tunnel managers.

Key words: CO concentration prediction, time series analysis, monitoring data, ARIMA model

CLC Number:

TU83

LU Jian-qiang, HE Xing, LIU Wei, LIN Hai-cheng, SUN Cheng-hui. Prediction of CO concentration in extra-long tunnels based on time series[J]. Journal of Lanzhou University of Technology, 2026, 52(3): 147-154.

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URL: https://journal.lut.edu.cn/EN/

https://journal.lut.edu.cn/EN/Y2026/V52/I3/147

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