基于反应时间的公路隧道接近段停车视距研究

文森; 梁波; 肖尧; 蒲俊勇

doi:10.3963/j.jssn.1674-4861.2021.02.006

基于反应时间的公路隧道接近段停车视距研究

doi: 10.3963/j.jssn.1674-4861.2021.02.006

文森^1,,
梁波^{1, 2, ,},
肖尧^{1, 3},
蒲俊勇¹

1.
重庆交通大学土木工程学院重庆 400074
2.
重庆交通大学山区桥梁与隧道工程国家重点实验室重庆 400074
3.
重庆市轨道交通（集团）有限公司重庆 400042

基金项目:

国家自然科学基金项目 51878107

详细信息

作者简介:
文森(1995—), 硕士研究生.研究方向: 隧道交通安全.E-mail: 1104208215@qq.com

通讯作者:
梁波(1964—), 博士, 教授.研究方向: 隧道与地下工程理论和技术.E-mail: liang_laoshi@126.com

中图分类号: U459.2
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出版历程
- 收稿日期: 2020-12-07

A Stopping Sight Distance in Access Zone of Highway Tunnel Based on the Reaction Time

LIANG Bo^1
,,
LIANG Bo^{1, 2
, ,},
XIAO Yao^{1, 3},
PU Junyong¹

1.
College of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China
2.
State Key Laboratory of Mountain and Bridge Engineering, Chongqing Jiaotong University, Chongqing 400074, China
3.
Chongqing Rail Transit(Group)Co. Ltd., Chongqing 400042, China

摘要

摘要: 反应时间是停车视距的1个重要影响因素，现有的停车视距模型较少考虑公路隧道洞外环境对驾驶员反应时间的影响。为研究公路隧道接近段的反应时间分布特性，选取25名驾驶人开展在公路隧道洞外不同距洞口距离、测试时刻和植被面积占比等条件下的室内仿真试验。利用公路隧道反应时间测量试验平台采集驾驶人的反应时间，应用重复测量方差分析对数据进行差异性显著检验，构建测试时刻、距洞口距离和反应时间关系度量模型，并在此基础上修正现有停车视距模型并进行验证。结果表明：①不同测点（距洞口距离）对应的反应时间存在总体显著性差异，反应时间随距洞口距离的减小呈先减小后增大的趋势，其中距离洞口40~60 m时反应时间达到最小；②不同植被面积占比对应的反应时间没有总体显著差异；③不同测试时刻对应的反应时间存在总体显著性差异，且测试时刻与测点间没有交互作用，反应时间随测试时刻增加呈先减小后增加的趋势，在16：00反应时间达到最小；④基于反应时间建立的新停车视距模型的校正系数为0.62，其计算值小于规范计算值，二者差值随设计速度的增加而增大；⑤经过新隧道实测值的验证，模型的预测值和实测值无显著差异，说明模型具有较好的预测能力。
- 交通安全 /
- 公路隧道 /
- 停车视距 /
- 室内仿真试验
Abstract: The reaction time is an important factor of stopping sight distance. The existing models of stopping sight distance seldom consider the influences of the environment outside the highway tunnel on reaction time of drivers. Twenty-five drivers are selected to carry out indoor simulation tests under different distances from the tunnel entrance, test time points, and vegetation area outside the tunnel to study the distribution of reaction time in the access zone of the highway tunnel. The platform for measurement of reaction time in the road tunnel is used to collect the reaction time of drivers, and ANOVA is used to test the significant differences in the data. A model of the relationship among the reaction time, distance, and time points is developed to quantify the impacts of distance and time on the reaction time. The results show that: ① The reaction time corresponding to different test points(distances from the entrance)has an overall significant difference. The reaction time first decreases and then increases with the decreased distance from the entrance, and reaches the minimum between 40 and 60 m from the entrance. ② The reaction timecorresponding to different vegetation area percentages has no overall significant difference. ③ The reaction time corresponding to different test time points has an overall significant difference, and there is no interaction between the test time point and the test point. The reaction time first decreases and then increases with the increase of time point, and reaches the minimum at 4 pm. ④ The correction coefficient of the new stopping sight distance model based on the reaction time is 0.62, with its calculated value less than the standard calculated value. The difference between the two increases with the increase of the design speed. ⑤ After verifying the measured value of the new tunnel, there is no significant difference between predicted and measured values, indicating that the model has a good predictive ability.
- traffic safety /
- highway tunnel /
- stopping sight distance /
- indoor simulation test

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图 1 洞外环境模拟装置和模拟驾驶设备

Figure 1. External environment simulated devices and simulated driving equipment

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图 2 反应时间测量程序界面

Figure 2. Measurement program interface to measure the response time

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图 3 不同方向的红色光斑示意图

Figure 3. Schematic diagram of red spots in different directions

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图 4 驾驶员反应时间布点

Figure 4. Distribution of drives'response time

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图 5 隧道仿真度评价

Figure 5. Evaluation on tunnel simulation

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图 6 测点与反应时间的关系

Figure 6. Relationship between the measuring points and reaction time

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图 7 不同植被面积占比与反应时间的关系

Figure 7. Relationship between different vegetation area proportions and reaction time

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图 8 隧道不同时刻的洞外亮度变化

Figure 8. Luminance changes outside the tunnel at different times

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图 9 测试时刻与反应时间的关系

Figure 9. Relationship between test time and reaction time

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图 10 反应时间与测试时刻和距隧道入口的距离综合作用下的三维曲面图

Figure 10. Three dimensional surface graph under the combined action of reaction time, test time, and distance from the tunnel entrance

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图 11 模型残差图

Figure 11. Residual plot of the model

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图 12 模型和规范的停车视距对比

Figure 12. Comparison of stopping sight distances between the model and specification

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图 13 停车视距散点图

Figure 13. Scatter plot of the stopping sight distance

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表 1 不同影响因素的总体显著性检验

Table 1. Overall significance test of different influencing factors

影响因素	统计量F	p
测点	19.785	0.000
测试时刻	7.032	0.000
植被面积占比	0.315	0.736
测点*测试时刻	0.915	0.583

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表 2 不同测点的反应时间均值

Table 2. Significance test between different measuring points

测点	反应时间均值/ms	标准差
测点1	905.35	47.32
测点2	881.99	48.40
测点3	835.12	36.78
测点4	833.32	34.56
测点5	917.87	47.58

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表 3 不同植被面积占比的反应时间均值

Table 3. Significance test among different vegetation area percentages

植被面积占比/%	反应时间均值/ms	标准差
48	868.03	40.19
50	886.43	70.48
62	869.73	50.19

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表 4 不同测试时刻的反应时间均值

Table 4. Significance test for different test time

测试时刻	反应时间均值/ms	标准差
10:00	901.62	58.30
12:00	875.29	57.36
13:00	883.71	55.94
14:00	882.49	63.04
15:00	891.71	52.61
16:00	823.38	22.97
17:00	864.88	36.99

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表 5 模型方差分析结果

Table 5. Variance analysis of the model

方差来源	自由度	平方和	均方	F值	p
回归	2	22 028.16	11 014.07	3.762	0.02
剩余	102	324 679.80	2 928.09
未修订和	104	349 974.90

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表 6 独立样本检验

Table 6. Independent sample test

量表	检验假设	方差方程的levene检验		均值方程的t检验
量表	检验假设	F检验值	显著性	t检验值	自由度	P值
停车视距	方差相等	17.82	0.001	-0.46	18	0.651
停车视距	方差不等			-0.46	9	0.656

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参考文献(25)

[1]	李文波, 刘东波, 谭山, 等. 山区双车道公路视距与交通安全的关系研究[J]. 黑龙江交通科技, 2007, 30(12): 149-151. doi: 10.3969/j.issn.1008-3383.2007.12.094 LI Wenbo, LIU Dongbo, TAN Shan, et al. Research on the relationship between sight distance and traffic safety in mountainous two-lane highways[J]. Heilongjiang Transportation Science and Technology, 2007, 30(12): 149-151. (in Chinese) doi: 10.3969/j.issn.1008-3383.2007.12.094
[2]	于亚敏. 公路隧道入口"黑洞"效应的数值表征与遮阳棚光环境设计方法[D]. 西安: 长安大学, 2019. YU Yamin. Numerical characterization of the "black hole" effect at the entrance of highway tunnels and the design method of the light environment of the awning[D]. Xi'an: Chang'an University, 2019. (in Chinese)
[3]	HAO S, WANG Q P, ZHANG P, et al. Spatialtemporal characteristics of tunnel traffic accidents in china from 2001 to present[J]. Advances in Civil Engineering, 2019(6): 1-12. http://www.researchgate.net/publication/345435237_Spatialtemporal_Characteristics_of_Tunnel_Traffic_Accidents_in_China_from_2001_to_Present
[4]	DELAIGUE P, ESKANDARIAN A. A comperehensive vehicle braking model for prediction of stopping distance[J]. Journal of Automobile Engineering, 2004, 218(12): 1409. doi: 10.1243/0954407042707641
[5]	MARIJONAS B, OLEG V. Efficiency of a braking process evaluating the roughness of road surface[J]. Transport, 2010, 21 (1): 3-7. doi: 10.1080/16484142.2006.9638032
[6]	WANG J J, XU F Y, MA A J. Research of safe speed at snow on freeway based on braking model of stopping sight distance[J]. Applied Mechanics & Materials, 2012, 209(12): 837-840. http://www.scientific.net/AMM.209-211.837
[7]	HABIB G, BUYRUK S. Improvement of safe stopping distance and accident risk coefficient based on active driver sight field on real road conditions[J]. IET Intelligent Transport Systems, 2019, 13(12): 1843-1850. doi: 10.1049/iet-its.2019.0322
[8]	袁浩, 史桂芳, 黄晓明, 等. 停车视距制动模型[J]. 东南大学学报(自然科学版), 2009, 39(4): 859-862. doi: 10.3969/j.issn.1001-0505.2009.04.041 YUAN Hao, SHI Guifang, HUANG Xiaoming, et al. Stopping sight distance braking model[J]. Journal of Southeast University(Natural Science Edition), 2009, 39(4): 859-862. (in Chinese) doi: 10.3969/j.issn.1001-0505.2009.04.041
[9]	姜虹, 李峰. 不同路面条件下高速公路的停车视距建模与安全车速分析[J]. 西安工业大学学报, 2012, 32(1): 25-30. doi: 10.3969/j.issn.1673-9965.2012.01.006 JIANG Hong, LI Feng. Stopping sight distance modeling and safe speed analysis of expressways under different road conditions[J]. Journal of Xi'an Technological University, 2012, 32 (1): 25-30. (in Chinese) doi: 10.3969/j.issn.1673-9965.2012.01.006
[10]	杨帆, 白浩晨, 贺亚龙, 等. 高速公路中央分隔带停车视距评价方法研究[J]. 公路交通科技, 2018, 35(6): 45-51. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201806007.htm YANG Fan, BAI Haochen, HE Yalong, et al. Research on the evaluation method of stopping sight distance in the central divider of expressway[J]. Highway Transportation Science and Technology, 2018, 35(6): 45-51. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201806007.htm
[11]	陈雨人, 付云天, 汪凡. 基于支持向量回归的视距计算模型建立和应用[J]. 中国公路学报, 2018, 31(4): 105-113. doi: 10.3969/j.issn.1001-7372.2018.04.013 CHEN Yuren, FU Yuntian, WANG Fan. Establishment and application of sight distance calculation model based on support vector regression[J]. China Journal of Highway and Transport, 2018, 31(4): 105-113. (in Chinese) doi: 10.3969/j.issn.1001-7372.2018.04.013
[12]	杜博英, 石红星. 竖曲线上的车速、视距及设计[J]. 公路交通技术, 2003(6): 8-10. doi: 10.3969/j.issn.1009-6477.2003.06.003 DU Boying, SHI Hongxing. Vehicle speed, sight distance and design on vertical curve[J]. Highway Traffic Technology, 2003 (6)8-10. (in Chinese) doi: 10.3969/j.issn.1009-6477.2003.06.003
[13]	李霖, 朱西产, 马志雄. 驾驶员在真实交通危险工况中的制动反应时间[J]. 汽车工程, 2014, 36(10): 1225-1229+1253. https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC201410014.htm LI Lin, ZHU Xichan, MA Zhixiong. Driver's braking reaction time in real traffic dangerous conditions[J]. Automotive Engineering, 2014, 36(10): 1225-1229+1253. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC201410014.htm
[14]	袁守利, 郭铮. 考虑驾驶员反应时间的车辆碰撞预警模型[J]. 安全与环境学报, 2021, 21(1): 270-276. https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ202101036.htm YUAN Shouli, GUO Zheng. Vehicle collision warning model considering driver reaction time[J]. Journal of Safety and Environment, 2021, 21(1): 270-276. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ202101036.htm
[15]	刘宁伟, 梁波, 肖尧, 等. 基于驾驶员反应时间特性的特长隧道行车安全[J]. 科学技术与工程, 2020, 20(26): 10927-10933. doi: 10.3969/j.issn.1671-1815.2020.26.056 LIU Ningwei, LIANG Bo, XIAO Yao, et al. Driving safety in extra-long tunnels based on the characteristics of the driver's reaction time[J]. Science Technology and Engineering, 2020, 20(26): 10927-10933. (in Chinese) doi: 10.3969/j.issn.1671-1815.2020.26.056
[16]	SHY B. Sight distance and horizontal curve aspects in the design of road tunnels vs. highways[J]. Tunnelling and Underground Space Technology, 2015, 45(02): 214-226. http://www.sciencedirect.com/science/article/pii/S0886779814001710
[17]	WARSHAWSKY L L, SHINAR D. Effects of uncertainty, transmission type, driver age and gender on braking reaction and movement time[J]. Journal of Safety Research, 2002, 33 (1): 117-128. doi: 10.1016/S0022-4375(02)00006-3
[18]	PENNETTI C A, HOLLENBACK K, KIM I, et al. Cognitive load variability from road characteristics should influence a safety requirement for vehicle stopping sight distance[J]. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2019, 63(1): 2129-2133. doi: 10.1177/1071181319631396
[19]	陈瑶. 公路景观对停车视距的影响研究[D]. 昆明: 昆明理工大学, 2015. CHEN Yao. Research on the impact of highway landscape on stopping sight distance[D]. Kunming: Kunming University of Science and Technology, 2015. (in Chinese)
[20]	闫寒. 采用LED光源的高速公路隧道智能照明技术研究[D]. 天津: 河北工业大学, 2017. YAN Han. Research on highway tunnel intelligent lighting technology using LED light source[D]. Tianjin: Hebei University of Technology, 2017. (in Chinese)
[21]	赵卫斌, 刘琦, 刘松荣, 等. 公路隧道洞外亮度时变特性研究[J]. 公路交通技术, 2017, 33(5): 132-135+145. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJT201705029.htm ZHAO Weibin, LIU Qi, LIU Songrong, et al. Research on the time-varying characteristics of the brightness outside the high⁃way tunnel[J]. Highway and Transportation Technology, 2017, 33(5): 132-135+145. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJT201705029.htm
[22]	胡英奎, 陈仲林, 张青文, 等. 驾车接近隧道过程中驾驶员的适应亮度变化规律[J]. 重庆大学学报, 2016, 39(1): 98-104. https://www.cnki.com.cn/Article/CJFDTOTAL-FIVE201601013.htm HU Yingkui, CHEN Zhonglin, ZHANG Qingwen, et al. The driver's adaptive brightness changes when driving close to the tunnel[J]. Journal of Chongqing University, 2016, 39(1): 98-104. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-FIVE201601013.htm
[23]	许景峰. 基于辐射度学的隧道洞外亮度及色温动态变化规律研究[D]. 重庆: 重庆大学, 2018. XU Jingfeng. Research on the dynamic changes of the brightness and color temperature outside the tunnel based on radiometry[D]. Chongqing: Chongqing University, 2018. (in Chinese)
[24]	胡馨月. 公路隧道照明对驾驶员动态视觉影响研究[D]. 西安: 长安大学, 2018. HU Xinyue. Research on the impact of highway tunnel lighting on driver's dynamic vision[D]. Xi'an: Chang'an University, 2018. (in Chinese)
[25]	CHOW S. Sample size calculations in clinical research[M]. London: Taylor & Francis, 2007.