Urban Tunnel Body Landscape Driving Comprehensive Evaluation Study Based on Biomass-Sensing Automobile Field Experiment

Abstract

Previously, in regard to tunnel design and research, the focus was primarily on traffic capacity and safety requirements, with less consideration given to cave landscape design and its impacts on drivers. This study addressed this gap by proposing a comprehensive evaluation system for urban tunnel landscape driving based on the Analytic Hierarchy Process (AHP) theory. Considering the information and perception aspects of the driving process and the unique landscape characteristics of urban tunnels, we utilized the drivers’ perception of biomass as an index layer and performed a simulation using a machine learning algorithm. The proposed model was validated through vehicle field tests that were conducted in four urban tunnels along with a substantial amount of measured biomass data obtained during the experiments. The research demonstrated a strong correlation between the urban tunnel body landscape and the driving comprehensive index, particularly under relevant biomass conditions, which revealed the interactive relationship between urban tunnel body landscape design parameters and biomass. Furthermore, the study analyzed and proposed the impact degree of the urban tunnel body landscape on drivers’ biomass indicators, which offered valuable insights into designing tunnel body landscapes with consideration for biomass perception.

Keywords

• urban tunnel
• tunnel body landscape
• biomass
• perception
• machine learning
• comprehensive evaluation