<p>Traditional expressway tunnel lighting systems usually lack sufficient consideration of the dynamic luminance demand arising from real-time traffic conditions and smooth luminance transitions between different tunnel lighting sections. These shortcomings may impede the effective accommodation of drivers’ visual adaptation during tunnel entry and transition and lead to unnecessary energy consumption due to over illumination. To address these issues, we propose in this study a deep learning based framework for tunnel luminance demand prediction. The standard luminance demand for each tunnel lighting section is first calculated according to the current national tunnel lighting standards. Then, a stepwise luminance demand division between tunnel lighting segments is achieved by using a combination of piecewise cubic Hermite interpolating polynomial and circuit segmentation, enabling a gradual transition of luminance between adjacent segments. Furthermore, taking the influence of variables such as environmental light intensity, traffic volume and vehicle speed on tunnel luminance demand into account, an attention-based dual LSTM model is developed to predict real-time tunnel luminance demand, so as to achieve intelligent “vehicle-following dimming” effect. The proposed model is validated by a case study from the Taolu Gou Tunnel on the Zhangcheng Expressway in Chengde, China. The model achieves an overall average R² exceeding 0.86 across all lighting segments, with all error metrics maintained at low levels. Compared to the BPNN, RNN, and conventional LSTM models, the prediction accuracy is improved by 13.02%, 19.66%, and 11.77%, respectively, which demonstrates its superior prediction performance within the investigated tunnel scenario and indicates its potential applicability under similar operational and environmental conditions.</p>