Influence of Horizontal and Vertical Alignments of Undersea Tunnel on Driver's Visual Characteristics and Vehicle Speed
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摘要: 海底隧道平纵线形组合方式复杂多样，容易导致驾驶人产生分心、疲劳等不良反应。为此，采集了30名驾驶人的实车数据，量化分析了海底隧道平纵线形对驾驶人视觉特征及车速的影响。运用Facelab眼动仪、GPS X10车载坡度计、行车记录仪等设备采集眨眼频率、单位时间内人眼闭合时间所占比例（percentage of eyeclosure over the pupil per unit time，PERCLOS）、隧道坡度、车速等数据。利用偏相关性分析得出海底隧道坡度、曲率与驾驶人眨眼频率、PERCLOS、车速的相关性及显著性，采用Ploy 2D非线性曲面拟合方法，分别建立眨眼频率、PERCLOS、车速与坡度-曲率的数学模型，量化分析眨眼频率、PERCLOS及车速与海底隧道平纵线形之间的关系，进而反映出海底隧道不同平纵线形组合对驾驶人的精神状态及行车状态的影响。结果表明：坡度1.3 %和圆曲线半径4 348 m的海底隧道平纵线形组合方式下，驾驶人的眨眼频率最大，精神最放松，适当增加上坡坡度值、减小圆曲线半径可以提高驾驶人的紧张感；坡度3.05 %和圆曲线半径3 521 m的海底隧道平纵线形组合方式下，驾驶人的PERCLOS最大，疲劳程度最高，适当增加下坡坡度值、减小圆曲线半径可以缓解驾驶人的疲劳；坡度1.78 %和圆曲线半径2 433 m的海底隧道平纵线形组合方式下，车速最快，适当增加上坡坡度值和圆曲线半径，可以降低车速。本文构建的视觉特征及车速模型，可以反映驾驶人的精神及行车状态随平纵线形的变化，为海底隧道平纵线形安全设计与运营管理提供理论支撑。Abstract: The combination of horizontal and vertical alignments of the undersea tunnel is complex and diverse, which can easily lead to adverse reactions such as distraction and fatigue for drivers. To this end, the data of 30 drivers are collected to quantify and analyze the effect of the cross-harbor tunnel flat and longitudinal alignment on drivers' visual characteristics and vehicle speed. The Facelab eye tracker, GPS X10 vehicle-mounted inclinometer, tachograph, and other equipment are used to collect the drivers' blink frequency, percentage of eye closure over the pupil per unit time (PERCLOS), tunnel slope, vehicle speed, and other data. The correlation and significance between the slope and curvature of the undersea tunnel and the drivers' blink frequency, PERCLOS, and vehicle speed are analyzed using partial correlation analysis. Then, Ploy 2D nonlinear surface fitting is used to establish mathematical models of blink frequency, PERCLOS, and vehicle speed with slope curvature. The relationship between blink frequency, PERCLOS, vehicle speed, and the horizontal and vertical alignments of the undersea tunnel is quantitatively analyzed, thus reflecting the influence of different combinations of horizontal and vertical alignments of the undersea tunnel on the driver's mental state and driving condition. Study results indicate that: Under the combination of a slope of 1.3% and the radius of the circular curve of 4 348 m, the drivers' blink frequency is highest, and their mental state is most relaxed. The driver's tension will be increased by appropriately increasing the positive slope and decreasing the curvature radius. Under the combination of a slope of 3.05% and the radius of the circular curve of 3 521 m, the drivers' PERCLOS is the largest and the fatigue degree is the highest. By appropriately increasing the negative slope and reducing the curvature radius the driver's tension will be relieved. Under the combination of a slope of 1.78% and the radius of the circular curve of 2 433 m, the vehicle speed is found to be the highest. The vehicle's speed will be reduced by appropriately increasing the positive slope and curvature radius. The constructed visual characteristics and speed model can reflect the changes in the driver's mental state and driving condition with the changes in horizontal and vertical alignments, which shall provide theoretical support for the horizontal and vertical alignments' safe design and operation management of the undersea tunnel.
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图 1 胶州湾海底隧道左线平曲线分布情况
Figure 1. Horizontal curve distribution of left line of undersea tunnel
图 2 胶州湾海底隧道左线纵坡变化情况
Figure 2. Longitudinal slope change of left line of undersea tunnel
图 4 海底隧道左线平纵线形、眨眼频率变化情况
Figure 4. Changes of horizontal and vertical alignments and blink frequency on the left line of undersea tunnel
图 5 海底隧道左线平纵线形、PERCLOS变化情况
Figure 5. Changes of horizontal and vertical alignments and PERCLOS on the left line of undersea tunnel
图 6 海底隧道左线平纵线形、车速变化情况
Figure 6. Changes of horizontal and vertical alignments and vehicle speed on the left line of undersea tunnel
图 7 胶州湾海底隧道左线平纵线形-眨眼频率拟合曲面
Figure 7. Horizontal and vertical alignment-blink frequency of the left line of undersea tunnel fitting surface
图 8 胶州湾海底隧道左线平纵线形-PERCLOS拟合曲面
Figure 8. Horizontal and vertical alignment-PERCLOS of the left line of undersea tunnel fitting surface
图 9 海底隧道左线平纵线形-车速拟合曲面
Figure 9. Horizontal and vertical alignment-vehicle speed of the left line of undersea tunnel fitting surface
表 1 胶州弯海底隧道左线平纵线形-眨眼频率偏相关性分析
Table 1. Partial correlation analysis of the horizontal and vertical alignment-blink frequency fitting model of the left line of undersea tunnel
控制变量 变量 相关性（r） 显著性（P） 曲率 下坡坡度 0.646 0.031 上坡坡度 -0.875 0.001 坡度 曲率 -0.842 < 0.001 
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表 2 胶州湾海底隧道左线平纵线形-眨眼频率拟合模型方差分析
Table 2. Variance analysis of the horizontal and vertical alignment-blink frequency fitting model of the left line of undersea tunnel
方差来源 平方和 均方 F值 P值 回归 71.970 11.995 2 105.274 < 0.001 残差 0.650 0.006 修正整体 2.350
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表 3 胶州湾海底隧道左线平纵线形-PERCLOS偏相关性分析
Table 3. Partial correlation analysis of the horizontal and vertical alignment-PERCLOS fitting model of the left line of undersea tunnel
控制变量 变量 相关性（r） 显著性（P） 曲率 坡度 0.902 < 0.001 坡度 曲率 -0.586 0.026
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表 4 胶州湾海底隧道左线平纵线形-PERCLOS拟合模型方差分析
Table 4. Variance analysis of the horizontal and vertical alignment-PERCLOS fitting model of the left line of undersea tunnel
方差来源 平方和 均方 F值 P值 回归 0.002 < 0.001 607.022 < 0.001 残差 0.001 < 0.001 修正整体 < 0.001
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表 5 胶州湾海底隧道左线平纵线形-车速偏相关性分析
Table 5. Partial correlation analysis of the horizontal and vertical alignment-vehicle speed fitting model of the left line of undersea tunnel
控制变量 变量 相关性（r） 显著性（P） 曲率 下坡坡度 0.741 < 0.001 上坡坡度 -0.923 < 0.001 坡度 曲率 0.564 0.016
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表 6 海底隧道左线平纵线形-车速拟合模型方差分析
Table 6. Variance analysis of the horizontal and vertical alignment-vehicle speed fitting model of the left line of undersea tunnel
方差来源 平方和 均方 F值 P值 回归 570 465.881 95 077.647 12 135.956 < 0.001 残差 893.119 7.834 修正整体 12 254.992
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