基于时间序列的特长隧道CO浓度预测

兰州理工大学学报 ›› 2026, Vol. 52 ›› Issue (3): 147-154.

基于时间序列的特长隧道CO浓度预测

路建强^*, 和星, 柳伟, 林海成, 孙成辉

中交基础设施养护集团宁夏工程有限公司, 宁夏银川 750001

收稿日期:2023-11-14 出版日期:2026-06-28 发布日期:2026-06-30
通讯作者: 路建强(1987-),男,宁夏银川人,高级工程师.Email:379451783@qq.com
基金资助:
宁夏回族自治区重点研发计划项目(2021BDE13005),自治区青年科技人才托举工程项目

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

LU Jian-qiang, HE Xing, LIU Wei, LIN Hai-cheng, SUN Cheng-hui

CCCC Infrastructure Maintenance Group Ningxia Engineering Co., Ltd., Yinchuan 750001, China

Received:2023-11-14 Online:2026-06-28 Published:2026-06-30

摘要/Abstract

摘要： 时间序列分析方法具有对时序数据进行建模和预测的优势,可准确揭示隧道内CO浓度的规律和趋势.通过收集六盘山隧道内连续三年的CO浓度、车辆流量、车速和风速等历史数据,采用时间序列分析方法构建CO浓度的预测模型,并对影响CO浓度变化的因素(交通流量、车速和风速)进行分析.结果表明:车辆流量的增加、车速的降低以及较低的风速可能导致CO浓度的升高;模型预测值与实测值的平均差异较小,表明预测模型具有一定的准确性和预测能力.预测模型的应用可为交通管理和环境保护提供科学依据,减少潜在的安全风险,为隧道管理者提供预警和决策.

关键词: CO浓度预测, 时间序列分析, 监测数据, ARIMA模型

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

中图分类号:

TU83

路建强, 和星, 柳伟, 林海成, 孙成辉. 基于时间序列的特长隧道CO浓度预测[J]. 兰州理工大学学报, 2026, 52(3): 147-154.

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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链接本文: https://journal.lut.edu.cn/CN/

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

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