Proposal for a Calculation Model of Perceived Luminance in Road Tunnel Interior Environment: A Case Study of a Tunnel in China

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.

Section: Hierarchical Establishment Of Evaluation Index Systemmentioning

confidence: 99%

“…Ocular data D7 Blink frequency [6,48] Blink frequency is used as the reference for comfort evaluation. Normal adults blink 10-20 times per minute.…”

Section: C3mentioning

confidence: 99%

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

Li,

Liang,

2023

“…The existence of such a model or method of determination would allow the photometric properties of aggregates to be considered in the design of an asphalt mix. The Qin article [14] uses a linear regression model in the form of multiple regression to calculate perceived luminance in the tunnel interior lighting environment. It was pointed out that this type of regression technique is only applicable to the object under study, and its generalisation requires consideration of more factors.…”

Section: Introductionmentioning

confidence: 99%

Modelling of the Luminance Coefficient in the Light Scattered by a Mineral Mixture Using Machine Learning Techniques

Mazurek,

Bąk-Patyna,

Ludwikowska-Kędzia

2024

The primary objective of the research and analysis reported in this article was to determine an effective method for predicting the luminance coefficient of the mineral mixture for asphalt concrete and stone mastic asphalt. The luminance of the mineral mixture determines the final luminance value of the surface. Predicting the luminance coefficient quickly will significantly improve the mineral–asphalt mix design efficiency in selecting aggregates that meet functional requirements and increase the brightness of the surface. The research process consisted of two stages. The first stage covered modelling the Qd luminance coefficient of aggregate, taking into account its petrographic analysis. The second fundamental stage, based on the research of the first stage, concerned the modelling of the luminance coefficient of the mineral mixture, taking into account the percentage share of a given component, its grain size, and its photometric properties. An effective technique of reinforced trees was used for modelling. As a result of its application, a model match to experimental data was achieved at the level of 87%. It has also been shown that the greatest impact on increasing the luminance coefficient of the mineral mixture was the use of light aggregate (quartzite sandstone or limestone) with a grain size of 2/5 in quantities > 40% or 8/11 in quantities > 60%. Furthermore, the quartzite sandstone aggregates with a grain size of 5/8 had the highest efficiency in lightening the mineral mixture. However, the use of basalt aggregates of the same fraction significantly worsened the photometric properties of the mineral mixture. An important element of the research was also to indicate that the mineralogical composition of the aggregate is crucial for an accurate assessment of its luminance coefficient.

“…It should be noted that it is possible to use traditional techniques, such as multiple regression, but these make it impossible to predict nonlinear effects. In the article by Qin [27], a multiple regression model was used to calculate the perceived luminance in tunnel interior lighting conditions. It was pointed out that this type of regression technique is applicable only to the object under study and its generalization requires the consideration of more factors.…”

Section: Introductionmentioning

confidence: 99%

Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate

Mazurek

Bąk-Patyna

2023

The primary purpose of the analysis presented here is to assess the feasibility of effectively predicting the aggregate luminance coefficient. Current road lighting standards and recommendations are based on assessing the level and distribution of luminance on the road surface. The brightness of a road surface depends on the amount of light falling on it, as well as the reflective properties of the road surface, which in turn depend on its physical condition, type and mineralogical composition. The complexity of the factors on which the value of the luminance coefficient depends it makes that data mining techniques the most appropriate tools for evaluation luminance coefficient phenomenon. This article uses five types of techniques: C&RT, boosted trees, random forest, neural network, and support vector machines. After a preliminary analysis, it was determined that the most effective technique was the boosted tree method. The results of the analysis indicated that the actual value of the luminance coefficient has multiple modal values within a single aggregate stockpile, depending on the mineralogical composition and grain size, and cannot be determined by a single central measure. The present model allowed us to determine the value of the luminance coefficient Qd with a mean error of 4.3 mcd-m−2·lx−1. In addition, it was found that the best aggregate for pavement brightening that allows high visibility during the day Qd and at night RL is a limestone aggregate. In the group of those that have the ability to potentially brighten the pavement were quartzite and granite aggregates.