The tunnel entrance represents a critical visual-environment interface, where tunnel access-zone luminance L 20 S directly impacts driver safety and overall tunnel lighting energy consumption. However, the L 20 S index is typically estimated using static values or simplified assumptions, which overlook the coupled effects of solar position, tunnel orientation, and portal geometry. To address this limitation, this study proposes a direction-sensitive, dynamic, modular and extensible framework based on 3D-printed scaled tunnel models, for the omnidirectional calculation and dynamic prediction of L 20 S . The framework achieves precise luminance prediction by correlating solar geometry with the luminance contributions of various outdoor scene components within the driver’s 20° field of view. The portal scene is decomposed into two components: the sky (including the sun) and the ground (including the road surface and vertical structures). For each component, the luminance response is modeled based on its specific geometric exposure to solar radiation. Sky luminance is calculated using the CIE general sky model, while ground-surface luminance is derived from irradiance-based regression models that distinguish direct-plus-diffuse and diffuse-only radiation states based on the angular relationship between solar azimuth and surface normal. Field measurements conducted on an urban tunnel in Beijing using the 3D-printed models demonstrate that this framework maintains average prediction errors within 10 % across various orientations, with maximum hourly errors under 20 %. This effectively captures time- and direction-dependent luminance variations. Overall, this framework provides a practical and cost-effective solution for real-time L 20 S estimation, facilitating a better balance between traffic safety and energy efficiency, and supporting adaptive tunnel lighting control.
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Xue et al. (2026) studied this question.
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