With the extension of highways to mountainous areas, ramp tunnels have inevitably emerged. Ramp tunnels, as a special type of tunnel structure, present unique safety concerns that involve both physical factors such as linear characteristics (radius, curvature), and speed-related aspects. Additionally, visual challenges are caused by the black–white hole effect. Consequently, research on driving safety within ramp tunnels remains insufficient. In order to explore the influence of ramp tunnel alignment on driving safety, based on the driver's information perception characteristics, key visual indicators, including fixation deviation, average fixation duration, blink frequency, saccade amplitude, and saccade speed, were collected through real-vehicle experiments. To establish a comprehensive driving safety model based on factor analysis and multiple visual indicators, and to evaluate the driving safety risks in ramp tunnels, and factor analysis method was used to quantify the driving safety, and combined with XGBOOST-SHAP, the influence of different physical quantities of ramp tunnel on ramp tunnel driving safety was revealed. The results show that: Saccade speed and vertical fixation deviation have the greatest influence on driving safety, followed by horizontal fixation deviation and average fixation time, and blink frequency and saccade amplitude have the least influence. The driving safety of the tunnel entrance section is reduced to the minimum value of 0.07 due to the ‘black hole effect ’. The interaction between the linear parameters and the dynamic light environment dominates the safety variation. The curvature parameter (SHAP value 0.43) and the road illumination change rate (SHAP value 0.35) jointly explain 72% of the safety fluctuation, while the influence weight of the design speed parameter is only 11%.
Zhao Long
. Driver Visual Workload Characteristics and Lafety Analysis under Ramp Tunnel Conditions[J]. Chinese Journal of Underground Space and Engineering, 2026
, 22(4)
: 1533
-1540
.
DOI: 10.20174/j.JUSE.2026.04.40
[1] 交通运输部. 2023年交通运输行业发展统计公报[R].北京:交通运输部,2024.(Ministry of Transport of the People's Republic of China. Statistical bulletin of the Development of the Transportation Industry in 2023 [R]. Beijing: Ministry of Transport of the People's Republic of China, 2024. (in Chinese))
[2] Shi L G, Li Y Q, Tao L L, et al. Sporters'visual comfort assessment in gymnasium based on subjective evaluation & objective physiological response[J]. Building and Environment, 2022, 225: 109678.
[3] 梁波, 牛佳安, 张红杰.基于扫视路径速度的隧道入口驾驶舒适性分析[J].地下空间与工程学报,2024,20(2):657-666. (Liang Bo, Niu Jiaan, Zhang Hongjie. Analysis on driving comfort at tunnel entrance based on saccade path speed[J]. Chinese Journal of Underground Space and Engineering, 2024, 20(2): 657-666. (in Chinese))
[4] Hamedani Z, Solgi E, Skates H, et al. Visual discomfort and glare assessment in office environments:a review of light-induced physiological and perceptual responses[J].Building and Environment, 2019, 153: 267-280.
[5] Wolfe B, Dobres J, Rosenholtz R, et al. More than the useful field: considering peripheral vision in driving[J]. Applied Ergonomics, 2017, 65: 316-325.
[6] 梁波, 董越, 闫自海, 等.基于眼动特性的隧道中间段光环境参数敏感性分析[J].交通信息与安全,2021,39(6):91-99. (Liang Bo, Dong Yue, Yan Zihai, et al. A sensitivity analysis of lighting condition parameters in the middle section of tunnel based on biomass index[J]. Journal of Transport Information and Safety, 2021, 39(6): 91-99. (in Chinese))
[7] 陈孟柯, 马健霄, 陆涛, 等.高速公路隧道行车视觉特性分析[J].交通信息与安全,2019,37(3):86-92. (Chen M K, Ma J X, Lu T, et al. Visual characteristics of drivers for driving through freeway tunnel[J]. Journal of Transport Information and Safety, 2019, 37(3): 86-92. (in Chinese))
[8] 梁波, 张红杰, 牛佳安, 等.基于扫视变化特性的城市隧道驾驶紧张情绪分析[J].安全与环境学报,2023.2023,23(10):3623-3631. (Liang Bo, Zhang Hongjie, Niu Jiaan, et al. Analysis of driving nervousness in urban tunnel based on saccade change characteristics[J]. Journal of Safety and Environment, 2023, 23(10): 3623-3631. (in Chinese))
[9] 高林.并行立交隧道施工顺序及近接影响分区研究[D].中南大学,2012. (Gao Lin. A parallel interchange tunnel construction sequenceand the adjacent influence subzone study[D]. Central South University, 2012. (in Chinese))
[10] 吴刚, 张俊平, 刘爱荣.地下互通立交结构的仿真分析研究[J].广州大学学报(自然科学版),2008,7(3):89-91. (Wu Gang, Zhang Jun Ping, Liu Airong. Simulation analysis and research of the underground interchange structure[J]. Journal of Guangzhou University(Natural Science Edition), 2008, 7(3): 89-91. (in Chinese))
[11] 刁志伟.隧道匝道安全优化设计技术研究[D].南京:东南大学,2018. (Diao Zhiwei. Technical research on the safety optimization design of the ramp inner tunnel[D]. Nanjing: Southeast University, 2018. (in Chinese))
[12] Pigliautile I, Casaccia S, Morresi N, et al. Assessing occupants'personal attributes in relation to human perception of environmental comfort: measurement procedure and data analysis[J]. Building and Environment, 2020, 177: 106901.
[13] Köhler A L, Koch I, Ladwig S. Guiding drivers towards safer driving speed: exploiting visual dominance in speed adaptation[J].Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 90: 438-450.
[14] 王海晓, 丁旭, 吕贞.基于注视行为特性的驾驶人分心负荷评估[J].华南师范大学学报(自然科学版),2022,54(4):7-17. (Wang Haixiao, Ding Xu, Lü Zhen, et al. The evaluation of driver's distraction load based on fixation behavior characteristics [J]. Journal of South China Normal University(Natural Science Edition), 2022, 54(4): 7-17. (in Chinese))
[15] 梁波, 秦灿, 牛佳安,等.基于TOPSIS改进因子分析的公路隧道入口段视觉负荷研究[J].中国安全科学学报,2024,34(1):77-84. (Liang Bo, Qin Can, Niu Jiaan, et al. Visual load in threshold zone of highway tunnel based on topsis improved factor analysis method[J]. China Safety Science Journal, 2024, 34(1): 77-84. (in Chinese))
[16] Wang S,Peng H,Liang S K.Prediction of Estuarine Water Quality Using Interpretable Machine Learning Approach[J].Journal of Hydrology,2022,605:127320.
