Study on the Design Parameters of Landscape Elements of Urban Tunnel Body Segments in China Based on Biomass Perception
Source abstract
Abstract
To provide a comprehensive analysis of how an intricate landscape environment comprising various landscape elements influences driving safety and comfort. Beginning with the individual design of the tunnel body section, which includes the roof, sidewalls, road surface, and sidewalk facade markings, this paper references the driver’s biomass index to identify design alterations that satisfy basic safety requirements through simulated driving experiments. Subsequently, employing orthogonal experimental design and semi-supervised clustering algorithms, we ascertain the optimal combination of the four main landscape elements of the tunnel body section that align with both safety and comfort objectives. The experimental findings demonstrate that a safe and comfortable landscape design for urban tunnel sections does not entail a single optimal design; rather, any landscape design conforming to the criterion of “patterned landscapes are set on the side walls and roofs of the tunnel instead of the surface of the road” is the optimal design.
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Research context
OpenAlex topics: Sustainable Urban and Rural Development · Land Use and Ecosystem Services · Wildlife-Road Interactions and Conservation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China
Funding
National Natural Science Foundation of China · Project of Chongqing Talent Team
References (34)
Show source-provided references
1. Sustainability of landscape in the portals of highway tunnels
2. Francesconi, E., and Degni, R. (2019). Tunnel entrances integrated into a landscape: The example of the Gronda motorway bypass. Tunnels and Underground Cities. Engineering and Innovation Meet Archaeology, Architecture and Art, CRC Press.10.1201/9780429424441-33 ↗
3. A study on effects of landscape design of road tunnel portal to interior lighting of tunnels10.9711/KTAJ.2013.15.5.497 ↗
4. Research on highway landscape design based on driver’s visual characteristics
5. Psychosocial factors, perceived risk and driving in a hostile environment: Driving through tunnels10.1504/IJGENVI.2008.017266 ↗
6. Ministry of Transportation and Communications (2021). Statistical Bulletin on Transportation Industry Development in 2020. Technical Report no. 581, Ministry of Transportation and Communications.
7. Wei, L., and Xuekan, Y. (2001). The safety of highway tunnel operation in Europe. Mod. Tunn. Technol., 28.
8. Learning to denoise astronomical images with U-nets10.1093/mnras/staa3567 ↗
9. Evaluation of comfort and safety of highway tunnel opening landscape design based on eye movement index
10. Research on visualization and comfort of entrances and exits of urban underwater very long tunnels
11. Research on the current situation and development trend of tunnel opening landscape
12. Interior landscape design of highway extra-long tunnels
13. Statistics of traffic accidents and evaluation of casualty status during the operation period of domestic highway tunnels
14. Research on the influence of tunnel arch wall decoration scheme on lighting effect
15. A review on evaluation and optimization of light environment in urban underwater extra-long tunnels based on visual demand
16. Prediction of lining response for twin tunnels constructed in anisotropic clay using machine learning techniques10.1016/j.undsp.2020.02.007 ↗
17. Ou, G. (2022). Research on Engineering Disaster Risk Assessment during Tunnel Construction Based on Machine Learning. [Ph.D. Thesis, China University of Geosciences].
18. Liu, Y. (2022). Research on Surface Settlement Prediction Model of Shallow Buried Tunnel Based on Machine Learning Algorithm. [Ph.D. Thesis, Chongqing Jiaotong University].
19. Hamerly, G., and Elkan, C. (2003). Learning the k in k-means. Adv. Neural Inf. Process. Syst., 16.
20. Li, Z., Liang, B., and Xu, M. (2023). Urban tunnel body landscape driving comprehensive evaluation study based on biomass-sensing automobile field experiment. Appl. Sci., 13.10.3390/app131911102 ↗
21. Vault design of highway tunnels based on the driver’s physiological characteristics10.1080/13467581.2021.1928503 ↗
22. The impact of tunnel design and lighting on the performance of attentive and visually distracted drivers10.1016/j.aap.2012.01.019 ↗
23. Study on driving safety evaluation based on tunnel side wall effect
24. Examining the influence of decorated sidewall in road tunnels using fMRI technology10.1016/j.tust.2020.103362 ↗
25. Wang, Y. (2020). Research on the Middle Section Lighting of Long Highway Tunnels Based on Driver Fatigue. [Ph.D. Thesis, Chongqing University].
26. Jiao, M. (2022). Research on Visual Characterization and Optimization Design of Highway Tunnel Light and Color Environment. [Ph.D. Thesis, Hebei University of Technology].
27. Evaluation of visual attractiveness of tunnels under colored pavement environment
28. How does tunnel interior color environment influence driving behavior? Quantitative analysis and assessment experiment10.1016/j.tust.2020.103320 ↗
29. Ministry of Transport of the People’s Republic of China (2024, July 01). Guidelines for Transportation Management, Available online: https://xxgk.mot.gov.cn/2020/jigou/kjs/202006/P020200623760741738973.pdf.
30. The influence of perceptual speed regulation on speed perception, choice, and control: Tunnel wall characteristics and influences10.1016/j.aap.2006.06.005 ↗
31. Tunnel lighting calculation model based on bidirectional reflectance distribution function: Considering the dynamic changes in light transmittance in road tunnels10.1016/j.tust.2023.105313 ↗
32. Long tunnel lighting environment improvement method based on multiple-parameter intelligent control: Considering dynamic changes in luminance difference10.1016/j.tust.2022.104637 ↗
33. Principal components analysis (PCA)10.1016/0098-3004(93)90090-R ↗
34. Ma, S., Hu, J., Ma, E., Li, W., and Wang, R. (2023). Cluster analysis of freeway tunnel length based on naturalistic driving safety and comfort. Sustainability, 15.10.3390/su151511914 ↗