Random Forests-Based Operational Status Perception Model in Extra-Long Highway Tunnels with Longitudinal Ventilation: A Case Study in China
Joint Authors
Chen, Jianxun
Luo, Yanbin
Qian, Chao
Li, Shuguang
Source
Journal of Advanced Transportation
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-05
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
An increasing number of extra-long highway tunnels have been built and put into operation around the world, but the quantified segmentation criteria for evaluating the in-tunnel operational status have not yet been enacted up till the present moment.
Meanwhile, ventilation facilities could not satisfy the dynamic requirements of fresh air demand under fast spatial-temporal variation of traffic conditions and operating environment.
In this study, the operational data collected from an extra-long highway tunnel were deeply analyzed using big data technology.
By combining traffic flow and environmental monitoring data, a data-driven perception model based on the Random Forests was structured.
The prediction results show that the proposed model provides better performances as compared to contrast models, indicating it had better ability to adapt to the dynamic changes of in-tunnel operational status while realizing accurate prediction.
The designed intelligent control strategies of ventilation facilities and traffic operation applying for different operational status would provide a theoretical basis and data support for lifting the level of intelligent control as well as promoting energy saving and consumption reducing in extra-long highway tunnels.
American Psychological Association (APA)
Qian, Chao& Chen, Jianxun& Luo, Yanbin& Li, Shuguang. 2018. Random Forests-Based Operational Status Perception Model in Extra-Long Highway Tunnels with Longitudinal Ventilation: A Case Study in China. Journal of Advanced Transportation،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1181355
Modern Language Association (MLA)
Qian, Chao…[et al.]. Random Forests-Based Operational Status Perception Model in Extra-Long Highway Tunnels with Longitudinal Ventilation: A Case Study in China. Journal of Advanced Transportation No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1181355
American Medical Association (AMA)
Qian, Chao& Chen, Jianxun& Luo, Yanbin& Li, Shuguang. Random Forests-Based Operational Status Perception Model in Extra-Long Highway Tunnels with Longitudinal Ventilation: A Case Study in China. Journal of Advanced Transportation. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1181355
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181355