公路隧道入口区域交通标志信息量对驾驶人视觉行为影响的实证研究

姜鲁青; 杜志刚; 麦晶

doi:10.3963/j.jssn.1674-4861.2025.02.003

公路隧道入口区域交通标志信息量对驾驶人视觉行为影响的实证研究

doi: 10.3963/j.jssn.1674-4861.2025.02.003

姜鲁青^1,,
杜志刚^{2, 3},
麦晶⁴

1.
青岛国信发展(集团)有限责任公司山东青岛 266071
2.
武汉理工大学交通与物流工程学院武汉 430063
3.
武汉理工大学交通信息与安全教育部工程研究中心武汉 430063
4.
佛山市佛照智城科技有限公司广东佛山 528000

基金项目:

国家自然科学基金项目 52072291

详细信息

作者简介:
姜鲁青（1972—），博士研究生. 研究方向：工程管理. E-mail: jiangluqing@qdgxjt.com

中图分类号: U491.2
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出版历程
- 收稿日期: 2024-06-02
- 网络出版日期: 2025-09-29

An Empirical Study on the Impact of Information Content of Traffic Signs in Entrance Areas of Highway Tunnels on Drivers' Visual Behavior

JIANG Luqing^1
,,
DU Zhigang^{2, 3},
MAI Jing⁴

1.
Qingdao Guoxin Development (Group) Co., Ltd., Qingdao 266071, Shandong, China
2.
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
3.
Engineering Research Center of Transportation Information and Safety of Ministry of Education, Wuhan University of Technology, Wuhan 430063, China
4.
Foshan Fozhao Zhicheng Technology Co., Ltd., Foshan 528000, Guangdong, China

摘要

摘要: 为评估公路隧道入口区域交通标志信息量对驾驶人视觉行为的影响，通过实驾试验，结合眼动熵与耦合协调度模型，探究了不同信息量等级对驾驶人视觉行为的影响规律，揭示了交通标志信息量与眼动特性的协调机制。试验选取6条公路隧道入口区域，分别对应6种交通标志信息量等级（T0~T5，0~81.18 bits），招募40名驾驶人佩戴眼动仪采集眼动数据，分析注视持续时间、扫视持续时间、扫视幅度指标，并基于样本熵理论计算眼动熵来分析视觉搜索模式的复杂性，构建耦合协调度模型以评估信息量与眼动行为的协调水平。结果表明：公路隧道入口区域交通标志信息量显著影响驾驶人眼动特性，随着信息量增加，注视与扫视持续时间呈先减后增趋势，扫视幅度先增后减；T3等级（48.31 bits）下各项指标表现最优，表明该等级下驾驶人信息感知与搜索效率最高；接近隧道过程中，注视、扫视持续时间及幅度的样本熵均逐渐增大，识别视距范围（125~100 m）增长速率显著提升，表明驾驶人对环境信息的搜索强度随距离缩短而增强；T3等级下眼动熵最小，视觉搜索模式最稳定；信息量与眼动行为的耦合协调度呈先升后降的单峰曲线，T3等级耦合协调度达0.851，处于“良好协调”水平（等级9），而信息量不足（T0~T1）或过载（T4~T5）时均处于失调状态。
- 交通工程 /
- 交通标志信息量 /
- 隧道入口 /
- 眼动熵 /
- 耦合协调度
Abstract: To evaluate the impact of information content of traffic signs in entrance areas of highway tunnels on drivers' visual behavior, this study conducted a real-driving experiment, combined with eye movement entropy and a coupling coordination degree model, with which the influence patterns of different levels of information content on drivers' visual behaviors is explored and the coordination mechanism between information content and characteristics of eye movement is revealed. The experiment selected six entrance areas of highway tunnels, representing six levels of information content of traffic signs (i.e., T0—T5, ranging from 0 to 81.18 bits). Forty drivers were recruited wearing eye trackers to collect eye movement data. Metrics such as fixation duration, saccade duration, and saccade amplitude were analyzed. Eye movement entropy was calculated based on sample entropy theory to analyze the complexity of visual search patterns, and a coupling coordination degree model was constructed to assess the coordination level between information content and eye movement behavior. The results indicated that the information content of traffic signs in the entrance areas of highway tunnels significantly affected drivers' eye movement characteristics. As the information content increased, fixation and saccade durations first decreased and then increased, while saccade amplitude first increased and then decreased. The T3 level (i.e., 48.31 bits) showed the best performance across all metrics, reflecting the highest efficiency in drivers' information perception and search. As vehicles approached the tunnel, the sample entropies of fixation duration, saccade duration, and saccade amplitude all gradually increased, and the rate of growth within the recognition sight distance range (125—100 m) significantly increased, indicating that drivers' search intensity for environmental information increased as the distance shortened. At the T3 level, eye movement entropy was the smallest, and the visual search pattern was the most stable. The coupling coordination degree between information content and eye movement behavior exhibited a unimodal curve that first increased and then decreased. The coupling coordination degree at the T3 level reached 0.851, which falls in a "good coordination" level (Level 9), while both levels of insufficient information (T0—T1) and information overload (T4—T5) resulted in a state of imbalance.
- traffic engineering /
- traffic signs information volume /
- tunnel entrance area /
- eye movement entropy /
- coupling coordination degree

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图 1 试验中的被试与眼动设备

Figure 1. The participant and eye-tracking device used during the experiment

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图 2 试验隧道场景

Figure 2. Scenarios of the experimental tunnels

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图 3 驾驶人眼动特征对比

Figure 3. Comparison of the driver's eye movement characteristics

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图 4 驾驶人眼动熵对比

Figure 4. Comparison of driver's eye movement SampEn

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图 5 耦合协调度变化趋势

Figure 5. Trends of the coupling coordination degree

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表 1 交通标志中各信息元素的信息量和权重

Table 1. The information volume and weight of each information element in traffic signs

信息元素	信息量/bits	权重
中文字符	11.8	0.25
英文字符	4.7	0.06
阿拉伯数字	3.3	0.15
几何图形	2.6	0.11
颜色	3.6	0.12
指向符号	4.9	0.22
图形符号	5.6	0.09

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表 2 试验隧道及信息量等级

Table 2. Test tunnels and TSIV levels

试验隧道	信息量等级	信息量/bits
（a）	T0	0
（b）	T1	29.62
（c）	T2	36.23
（d）	T3	48.31
（e）	T4	64.34
（f）	T5	81.18

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表 3 耦合协调度等级的划分标准

Table 3. Classification criteria of the coupling coordination level

耦合协调度D值区间	协调等级	耦合协调程度
（0.0~0.1）	1	极度失调
[0.1~0.2）	2	严重失调
[0.2~0.3）	3	中度失调
[0.3~0.4）	4	轻度失调
[0.4~0.5）	5	濒临失调
[0.5~0.6）	6	勉强协调
[0.6~0.7）	7	初级协调
[0.7~0.8）	8	中级协调
[0.8~0.9）	9	良好协调
[0.9~1.0）	10	优质协调

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表 4 耦合协调度结果

Table 4. The results of the coupled coordination degree

信息量等级	耦合度C值	协调度T值	耦合协调度D值	协调等级	耦合协调程度
T0	0.22	0.393	0.294	3	中度失调
T1	0.311	0.439	0.369	4	轻度失调
T2	0.936	0.593	0.745	8	中级协调
T3	0.945	0.766	0.851	9	良好协调
T4	0.914	0.583	0.729	7	初级协调
T5	0.322	0.497	0.403	5	濒临失调

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参考文献(19)

[1]	MA Z, SHAO C, ZHANG S. Characteristics of traffic accidents in Chinese freeway tunnels[J]. Tunnelling and underground space technology, 2009, 24(3): 350-355. doi: 10.1016/j.tust.2008.08.004
[2]	韩磊, 杜志刚, 潘远轩, 等. 基于文献计量分析的公路隧道交通安全国际研究进展[J]. 交通信息与安全, 2024, 42(6): 1-13. doi: 10.3963/j.jssn.1674-4861.2024.06.001 HAN L, DU Z G, PAN Y X, et al. International research progress on highway tunnel traffic safetybased on bibliometric analysis[J]. Journal of Transport Information and Safety, 2024, 42(6): 1-13. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2024.06.001
[3]	COSTA A T, FIGUEIRA A C, LAROCCA A, 2022. An eye-tracking study of the effects of dimensions of speed limit traffic signs on a mountain highway on driver's perception[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 87: 42-53. doi: 10.1016/j.trf.2022.03.013
[4]	DU J, REN G, LIU W, et al. How is the visual working memory load of driver influenced by information density of traffic signs?[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 86: 65-83. doi: 10.1016/j.trf.2022.02.007
[5]	GWYTHER H, HOLLAND C. Feelings of vulnerability and effects on driving behaviour: a qualitative study[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2014, 24: 50-59. doi: 10.1016/j.trf.2014.03.001
[6]	JIAO F, DU Z, CHEN G, et al. Entrance zone length of extra-long undersea tunnels based on vision adaptation[J]. Tunnelling and Underground Space Technology, 2021, 113: 103970. doi: 10.1016/j.tust.2021.103970
[7]	FANG Y, ZHOU J, HU H, et al. Combination layout of traffic signs and markings of expressway tunnel entrance sections: A driving simulator study[J]. Sustainability, 2022, 14(6): 3377. doi: 10.3390/su14063377
[8]	SHANG T, WU P, LIAN G, et al. Influences of exit advance guide signs on the trajectory and speed of passenger cars in highway tunnels[J]. Journal of Advanced Transportation, 2021 (2): 1-14.
[9]	LU H, SHANG T, WEI Y, et al. Safety assessment of exit advance guide signs in mountainous highway tunnel based on eye-tracking technology[J]. IEEE Access, 2021 (9) : 111315-111325.
[10]	SHANG T, LU H, WU P, et al. Method of setting exit advance guide signs in highway tunnels based on the driver's eye movement with markov chains[J]. IEEE Access, 2021 (9): 24079-24092.
[11]	LYU N C, XIE L, WU C Z, et al. Driver's cognitive workload and driving performance under traffic sign information exposure in complex environments: a case study of the highways in China[J]. International Journal of Environmental Research and Public Health, 2017, 14(2): 203. doi: 10.3390/ijerph14020203
[12]	LIU K, DENG H. The relationship of the information quantity of urban roadside traffic signs and drivers' visibility based on information transmission[J]. International Journal of Environmental Research and Public Health, 2021, 18(20): 10976. doi: 10.3390/ijerph182010976
[13]	韩磊, 朱守林, 高明星, 等. 注视熵和马尔科夫链的弯道诱导设施信息量研究[J]. 中国安全科学学报, 2020, 30(8): 122-128. HAN L, ZHU S L, GAO M X, et al. Research on information volume of guidance facilities on bends based on gaze entropy and Markov chain[J]. China Safety Science Journal, 2020, 30(8): 122-128. (in Chinese)
[14]	韩磊, 朱守林, 戚春华, 等. 草原公路弯道交通工程设施信息量评价[J]. 中国安全科学学报, 2019, 29(4): 83-88. HAN L, ZHU S L, QI C H, et al. Evaluation of traffic engineering facilities information volume of prairie highway curve[J]. China Safety Science Journal, 2019, 29(4): 83-88. (in Chinese)
[15]	吕能超, 曹越, 秦羚, 等. 基于交通标志信息量的驾驶负荷加载有效性研究[J]. 中国公路学报, 2018, 31(8): 165-172. LYU N C, CAO Y, QIN L, et al. Research on the effectiveness of driving workload based on traffic sign information volume[J]. China Journal of Highway and Transport, 2018, 31(8): 165-172. (in Chinese)
[16]	YAN G, WANG M, QIN P, et al. Comparative study on drivers' eye movement characteristics and psycho-physiological reactions at tunnel entrances in plain and high-altitude areas: a pilot study[J]. Tunnelling and Underground Space Technology, 2022, 122: 104370. doi: 10.1016/j.tust.2022.104370
[17]	BARA C, PERNICE R, CATANIA C A, et al. Comparison of entropy rate measures for the evaluation of time series complexity: Simulations and application to heart rate and respiratory variability[J]. Biocybernetics and Biomedical Engineering, 2024, 44(2): 380-392. doi: 10.1016/j.bbe.2024.04.004
[18]	AKTARUZZAMAN M, SASSI R. Parametric estimation of sample entropy in heart rate variability analysis[J]. Biomedical Signal Processing and Control, 2014, 14: 141-147. doi: 10.1016/j.bspc.2014.07.011
[19]	黄文成, 帅斌, 庞璐, 等. 基于耦合协调度的道路危险品运输系统风险评价[J]. 中国安全科学学报, 2016, 26(6): 117-122. HUANG W C, SHUAI B, PANG L, et al. Research on coupling coordination degree based method for assessing risk in road dangerous goods transport system[J]. China Safety Science Journal, 2016, 26(6): 117-122. (in Chinese)