Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (7): 1904-1914.doi: 10.13229/j.cnki.jdxbgxb.20241361

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Driving comfort in tunnel groups based on longitudinal spacing and brightness difference

Yun-wei MENG1(),Zhi-peng LI1,Yin ZHANG2,Zhen-yu QUAN1,Guang-qing YANG3,Fang CHEN3,Si-jing LAI4,Jun QIAO5   

  1. 1.Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System，Chongqing Jiaotong University，Chongqing 400074，China
    2.College of Civil Engineering，Chongqing Jiaotong University，Chongqing 400074，China
    3.Institute of Road Engineering，China Merchants Chongqing Communications Technology Research & Design Institution Co. Ltd. ，Chongqing 400067，China
    4.China Merchants Roadway Information Technology （Chongqing） Co. Ltd. ，Chongqing 400067，China
    5.China Railway Siyuan Survey and Design Group Co. Ltd. ，Wuhan 430063，China
  • Received:2024-12-26 Online:2026-07-01 Published:2026-08-12

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Abstract:

To investigate the variation law of driving comfort on expressway tunnel group sections， real vehicle tests were carried out to collect the tunnel section length， regional luminance， and drivers' real-time heart rate.The relationships among tunnel spacing， environmental luminance difference， and heart rate growth rate were analyzed. Based on the mechanism by which tunnel spacing affects the downstream heart rate response， the concept of the tunnel group psychological effect was proposed. Driving comfort was characterized by heart rate growth rate， and a comfort model was developed under the coupling of luminance difference and tunnel spacing. The mechanism underlying the tunnel group psychological effect was also explored. The results show that as tunnel spacing increases， the heart rate growth rate first rises and then falls. A longer tunnel spacing intensifies the driving load in downstream tunnels and reduces comfort. The heart rate growth rate increases with the environmental luminance difference. The coupling of tunnel spacing and luminance difference significantly affects driving comfort. A greater luminance difference corresponds to a wider tunnel spacing range in which the psychological effect is likely to occur.

Key words: engineering of communications and transportation system, psychological effect in tunnel group, real vehicle test, driving comfort, longitudinal distance, environmental brightness differences

CLC Number: 

  • U458.1

Table 1

Driver data statistics"

基本信息平均值标准差取值
年龄/岁36.59.824~49
驾驶年限/年11.26.45~19
驾驶里程/km91 625113 98425 600~300 000
日均驾驶时长/（h·d-1）3.11.61.0~6.0

Fig.1

Instruments used in the experiment"

Fig.2

Entrance and exit of tunnel group"

Fig.3

Experimental tunnel group section"

Table 2

Overview of tunnel groups"

隧道群编号组成隧道名称及长度纵向间距/m
①中兴隧道（6 000 m）-银盘隧道（1 100 m）30
②白马隧道（3 100 m）-羊角隧道（6 656 m）80
③羊角隧道（6 656 m）-大湾隧道（2 800 m）190
④对照组董家湾隧道（1 000 m）-望江寺隧道（667 m）300

Fig.4

Research scope of tunnel group"

Fig.5

Experimental process"

Fig.6

Heart rate growth rate of tunnel groups ① to ④"

Fig.7

Comparison of heart rate growth chart for various tunnel groups"

Fig.8

Longitudinal spacing fitted to heart rate growth rate"

Fig.9

Relationship between brightness difference andheart rate growth rate"

Fig.10

Fitting of environmental brightness differenceand heart rate growth rate"

Table 3

Cluster center division"

分类舒适较舒适不舒适
聚类中心23.2%24.9%26.8%

Table 4

Comfort level in tunnel group"

舒适度等级心率增长率/%驾驶负荷
舒适<24低
较舒适［24，26.3）中
不舒适≥26.3高

Fig.11

Heart rate growth rate under the coupling oflongitudinal spacing and environmental brightness difference"

Table 5

Fitting and validation results"

方法决定系数R2均方根误差RMSE/%
二维多项式函数0.93-
10折交叉验证法0.820.31

Table 6

Longitudinal distance range of psychological effects in tunnel groups"

序号测试时间段环境亮度差/（cd·m-2）纵向间距/m
1-500-
217：00~19：00813-
317：00~19：001 620-
49：00~11：002 100-
59：00~11：002 670［103，112）
613：00~15：003 670［76，122）
713：00~15：004 600［61，153）
8-5 000［54，160］
[1] 吕能超, 王玉刚, 周颖, 等. 道路交通安全分析与评价方法综述[J]. 中国公路学报, 2023, 36(4): 183-201.
Neng-chao Lyu, Wang Yu-gang, Zhou Ying, et al. Review of road traffic safety analysis and evaluation methods[J]. China Journal of Highway and Transport, 2023, 36(4): 183-201.
[2] Wan L, Yan Y, Zhang C, et al. Characteristics and identification of risky driving behaviors in expressway tunnels based on behavior spectrum[J]. International Journal of Transportation Science and Technology, 2024, 16: 5-17.
[3] 潘福全, 泮海涛, 王铮, 等. 照度与纵坡耦合作用下海底隧道出入口段驾驶人视觉特征分析与建模[J]. 交通运输系统工程与信息, 2021, 21(1): 142-148.
Pan Fu-quan, Pan Han-tao, Wang Zheng, et al. Analysis and modeling of drivers' visual characteristics at entrance and exit of undersea tunnel with coupling of illuminance and longitudinal slope[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(1): 142-148.
[4] 朱彤, 吴玲, 胡月琦, 等. 基于因子模型的高速公路特长隧道驾驶人心理负荷特性研究[J]. 中国公路学报, 2018, 31(11): 165-175.
Zhu Tong, Wu Ling, Hu Yue-qi, et al. Research on characteristics of drivers' mental workload in extra-long expressway tunnels based on the factor model[J]. China Journal of Highway and Transport, 2018, 31(11): 165-175.
[5] Yang Y Z, Du Z G, Alonso F, et al. Why frequent traffic accidents at highway tunnel exit?—An experimental analysis of the slack effect[J]. Tunnelling and Underground Space Technology, 2024, 152: No.105927.
[6] 孟云伟, 全振宇, 王子骁, 等. 亮度和速度协同下公路隧道口路段驾驶舒适度研究[J]. 交通运输系统工程与信息, 2024, 24(4): 116-126.
Meng Yun-wei, Quan Zhen-yu, Wang Zi-xiao, et al. Driving comfort on highway tunnel portal sections based on coordination of luminance and speed[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(4): 116-126.
[7] Jiao F T, Du Z G, Chen G J, et al. Entrance zone length of extra-long undersea tunnels based on vision adaptation[J]. Tunnelling and Underground Space Technology, 2021, 113: No.103970.
[8] Pervez A, Lee J, Huang H L. Exploring factors affecting the injury severity of freeway tunnel crashes: a random parameters approach with heterogeneity in means and variances[J]. Accident Analysis & Prevention, 2022, 178: No.106835.
[9] 张姝玮, 郭忠印, 杨轸, 等. 驾驶行为多重分形特征在驾驶疲劳检测中的应用[J]. 吉林大学学报: 工学版, 2021, 51(2): 557-564.
Zhang Shu-wei, Guo Zhong-yin, Yang Zhen, et al. Application of multi-fractal features of driving performance in driver fatigue detection[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(2): 557-564.
[10] Chen S F, Lin Y, Zhao H T. Effects of flicker with various brightness contrasts on visual fatigue in road lighting using fixed low-mounting-height luminaires[J]. Tunnelling and Underground Space Technology, 2023, 136: No.105091.
[11] 崔洪军, 王玉帛, 朱敏清, 等. 隧道群明暗转换对暗反应时间的影响[J]. 交通运输工程学报, 2021, 21(2): 200-208.
Cui Hong-jun, Wang Yu-bo, Zhu Min-qing, et al. Influence of tunnel group light-dark conversion on dark reaction time[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 200-208.
[12] 王首硕, 杜志刚, 冯守中, 等. 高速公路隧道入口区域视线诱导系统有效性研究[J]. 交通运输工程学报, 2021, 21(2): 267-277.
Wang Shou-shuo, Du Zhi-gang, Feng Shou-zhong, et al. Research on effectiveness of visual guiding system in entrance zone of freeway tunnel[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 267-277.
[13] Prithila A, Behnam B, Zunayed M, et al. Multimodal brain-computer interface for in-vehicle driver cognitive load measurement: dataset and baselines[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(6): 5949-5964.
[14] Zheng H R, Du Z G, Jia C F, et al. Evaluating the effectiveness of rhythmic visual guidance technology for mitigating driving risks in highway tunnel groups: a simulation study[J]. Accident Analysis & Prevention, 2025, 212: No.107940.
[15] Liang B, Wong Y D, He S Y, et al. Dynamic traffic safety risk assessment in road tunnel entrance zone based on drivers' psychophysiological perception states: methodology and case-study insights[J]. Tunnelling and Underground Space Technology, 2024, 147: No.105677.
[16] 叶飞, 苏恩杰, 梁晓明, 等. 公路隧道景观设计现状和思考[J]. 中国公路学报, 2022, 35(1): 23-37.
Ye Fei, Su En-jie, Liang Xiao-ming, et al. Review and thinking on landscape design of highway tunnel[J]. China Journal of Highway and Transport, 2022, 35(1): 23-37.
[17] Lyu N, Wang Y, Wu C, et al. Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions[J]. Journal of Intelligent and Connected Vehicles, 2022, 5(1): 17-35.
[18] 徐进, 陈正欢, 廖祺硕, 等. 基于心电数据的高速公路高密度互通立交驾驶负荷[J]. 吉林大学学报: 工学版, 2024, 54(10): 2807-2818.
Xu Jin, Chen Zheng-huan, Liao Qi-shuo, et al. Mental workload of drivers at high-density interchanges of freeways based on ECG data[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(10): 2807-2818.
[19] 方守恩, 邬洪波, 廖军洪, 等. 山区高速公路隧道群路段安全评价[J]. 同济大学学报: 自然科学版, 2013, 41(5): 693-699.
Fang Shou-en, Wu Hong-bo, Liao Jun-hong, et al. Safety evaluation of freeway tunnel groups in mountainous areas[J]. Journal of Tongji University(Natural science), 2013, 41(5): 693-699.
[20] 段萌萌, 唐伯明, 胡旭辉, 等. 高隧道比路段隧道出入口驾驶员视觉负荷研究[J]. 交通运输系统工程与信息, 2018, 18(3): 113-119.
Duan Meng-meng, Tang Bo-ming, Hu Xu-hui, et al. Driver's visual load at tunnel entrance and exit of sections with high ratio of tunnels[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(3): 113-119.
[21] 蔡晓禹, 雷财林, 彭博, 等. 基于驾驶行为和信息熵的道路交通安全风险预估[J]. 中国公路学报, 2020, 33(6): 190-201.
Cai Xiao-yu, Lei Cai-lin, Peng Bo, et al. Estimation of road traffic safety risk based on driving behavior and information entropy[J]. China Journal of Highway and Transport, 2020, 33(6): 190-201.
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