Evaluation of eye-catching effect of highway tunnel entrance zones based on factor analysis and data envelopment analysis
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摘要: 为全面评价公路隧道入口区域视觉吸引对驾驶人吸睛效应的影响，招募了30名被试开展自然驾驶试验，采集驾驶人在公路隧道入口区域不同视觉吸引条件下的眼动和心电数据，基于因子分析法选取吸睛效应敏感评价指标，并构建数据包络分析模型，识别和探究公路隧道入口区域视觉吸引条件对驾驶人吸睛效应的影响特征和作用机理。分析结果表明：驾驶人吸睛效应的视觉特性敏感指标为注视持续时间(FD1)、瞳孔直径(PD)、扫视持续时间(SD)和扫视幅度(SR)，心电特性敏感指标为心率(HR)、低频与高频比值R、样本熵(SampEn)和分形维数(FD2)；公路隧道入口区域不同视觉吸引条件对驾驶人吸睛效应影响显著，其中，警示标识条件下，驾驶人的平均FD1(530.97±37.03 ms)、PD(4.56±0.46 mm)、SD(32.89±3.14 ms)显著增加，SR(4.77°±1.27°)显著降低，同时HR(96.64±9.23次·min-1)、R(4.17±0.98)和FD2(1.87±0.17)也显著升高，SampEn(1.84±0.24)显著降低，表明警示标识条件下驾驶人对视觉信息的感知和处理表现较差，视觉认知负荷和心理压力程度较大，心理状态的稳定性程度受到较大的负面影响；不同视觉吸引条件对驾驶人吸睛效应的综合效率具有显著影响，其中无显著视觉吸引场景的效率均值最高，为0.987，警示标识场景的效率均值最低，为0.928，且各场景之间差异显著；受驾驶人吸睛效应影响最大的变量是扫视幅度和心率，其中，警示标识对驾驶人的视觉特性和心理负荷水平的负面影响最大。研究成果可为公路隧道入口区域视觉环境优化设计提供参考，有助于有效管控该区域的行车风险。Abstract: To comprehensively evaluate the impact of visual attractions in highway tunnel entrance zones on drivers' eye-catching effect, 30 participants were recruited for a naturalistic driving experiment. Eye movement and electrocardiogram (ECG) data were collected under different visual attraction conditions in the highway tunnel entrance zones. Sensitive evaluation indicators for the eye-catching effect were selected based on factor analysis. A data envelopment analysis (DEA) model was constructed to identify and explore the characteristics and mechanisms of how visual attraction conditions in highway tunnel entrance zones influence drivers' eye-catching effect. Analysis results show that the sensitive indicators for drivers' eye-catching effect in terms of visual characteristics are fixation duration(FD1), pupil diameter(PD), saccade duration(SD), and saccade range(SR), while the sensitive indicators for ECG characteristics are heart rate (HR), ration of low-frequency to high-frequency ratio (R), sample entropy (SampEn), and fractal dimension (FD2). Drivers' eye-catching effect is significantly affected by different visual attraction conditions in highway tunnel entrance zones. Specifically, under warning sign conditions, drivers exhibit significantly increase in average FD1 (530.97±37.03 ms), PD (4.56±0.46 mm), and SD (32.89±3.14 ms), along with a significant decrease in SR (4.77°±1.27°). Concurrently, a significant rise can be seen in HR (96.64±9.23 beates per minute), R (4.17±0.98), and FD2 (1.87±0.17), while a decline is found in SampEn (1.84±0.24). These findings suggest that under warning sign conditions, drivers' perception and processing of visual information are impaired, with higher visual cognitive load and psychological stress, leading to greater instability in their psychological state. Different visual attraction conditions have a significant impact on the overall efficiency of drivers' eye-catching effect. The mean efficiency is the highest (0.987) in a scenario with no significant visual attraction and the lowest (0.928) in a warning sign scenario, with significant differences observed among various scenarios. Saccade range and HR are the variables most affected by drivers' eye-catching effect. The warning sign has the most negative impact on drivers' visual characteristics and psychological load levels. The research findings provide valuable insights for optimizing the visual environment design of highway tunnel entrance zones, contributing to effective management and control of driving risks in such zones.
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Key words:
- traffic safety /
- eye-catching effect /
- tunnel entrance /
- factor analysis /
- data envelopment analysis
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图 7 基于DEA的公路隧道入口区域吸睛效应评价体系
Figure 7. Evaluation system of eye-catching effect of highway tunnel entrance zone based on DEA
图 8 不同场景下驾驶人吸睛效应的综合效率均值
Figure 8. Average comprehensive efficiency of driver's eye-catching effect in different scenarios
表 1 方差解释结果
Table 1. Interpretation result of variance
成分 眼动指标 心电指标 初始特征值 提取后的特征值 方差贡献率/% 累积贡献率/% 初始特征值 提取后的特征值 方差贡献率/% 累积贡献率/% 1 4.567 4.345 56.662 56.662 4.312 3.891 43.236 43.236 2 2.345 2.123 26.562 83.224 1.762 1.584 38.546 81.782 3 1.014 0.912 8.234 91.458 0.974 0.877 9.246 91.028 4 0.901 0.747 3.856 95.314 0.658 0.592 4.537 95.565 5 0.688 0.521 2.264 97.578 0.542 0.488 2.323 97.888 6 0.419 0.226 1.176 98.754 0.402 0.362 1.024 98.912 7 0.322 0.092 0.858 99.612 0.268 0.241 0.673 99.585 8 0.174 0.028 0.343 99.955 0.156 0.141 0.362 99.947 9 0.084 0.007 0.045 100.000 0.125 0.113 0.053 100.000 
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表 2 因子成分
Table 2. Factor components
指标类型 指标 成分1 成分2 眼动 FD1 0.931 0.234 FN 0.567 0.390 PD 0.889 0.678 SD 0.456 0.923 SR 0.234 0.956 SV 0.365 0.234 SN 0.434 0.567 BD 0.398 0.401 BN 0.382 0.464 心电 HR 0.849 -0.284 SDNN -0.686 0.329 RMSSD -0.657 0.311 PNN50 -0.642 0.303 LF 0.772 0.463 HF -0.699 -0.351 R 0.912 -0.139 SampEn -0.701 0.811 FD2 0.753 0.892
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表 3 眼动指标差异性分析结果
Table 3. Results of eye movement indicators difference analysis
场景 平均注视持续时间 平均瞳孔直径 平均扫视持续时间 平均扫视幅度 均值(标准差)/ms F、p、η2 均值(标准差)/mm F、p、η2 均值(标准差)/ms F、p、η2 均值(标准差)/(°) F、p、η2 A 373.16(23.86) F=164.57
p＜0.01
η2=0.813.77(0.27) F=24.5
p＜0.01
η2=0.3924.11(2.62) F=71.86
p＜0.01
η2=0.656.57(0.74) F=17.33
p＜0.01
η2=0.31B 420.06(25.13) 4.07(0.30) 27.38(2.09) 5.92(0.83) C 530.97(37.03) 4.56(0.46) 32.89(3.14) 4.77(1.27) D 434.63(25.08) 4.23(0.39) 30.94(2.02) 5.03(1.37)
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表 4 心电指标差异性分析结果
Table 4. Results of ECG indicators difference analysis
场景 HR R SampEn FD2 均值/(次·min-1) F、p、η2 均值 F、p、η2 均值 F、p、η2 均值 F、p、η2 A 73.09(8.81) F=36.66
p＜0.01
η2=0.491.75(0.94) F=34.29
p＜0.01
η2=0.472.41(0.39) F=22.14
p＜0.01
η2=0.361.43(0.16) F=40.68
p＜0.01
η2=0.51B 84.77(9.15) 3.06(0.97) 2.02(0.27) 1.67(0.15) C 96.64(9.23) 4.17(0.98) 1.84(0.24) 1.87(0.17) D 90.71(9.19) 3.62(0.97) 1.95(0.25) 1.75(0.17)
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表 5 不同场景下各决策单元效率评价结果
Table 5. Efficiency evaluation results of each DMU in different scenarios
DMU 效率 场景A 场景B 场景C 场景D 1 1.000 1.000 0.911 0.943 2 1.000 0.938 0.991 1.000 ⋮ ⋮ ⋮ ⋮ ⋮ 29 0.977 0.947 1.000 1.000 30 1.000 0.955 0.893 0.924
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表 6 不同场景的DEA有效性
Table 6. DEA effectiveness for different scenarios
场景 技术效益 规模效益 综合效益 规模报酬系数 输入指标松弛变量 输出指标松弛变量 有效性 A 1.00 1.00 1.00 1.000 0.00 0.0 DEA强有效 B 0.90 1.00 0.90 0.592 2.10 5.0 非DEA有效 C 0.70 0.86 0.60 0.326 4.40 10.3 非DEA有效 D 0.95 1.00 0.95 0.637 1.05 3.0 非DEA有效
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表 7 不同场景的DEA松弛变量分析
Table 7. DEA relaxation variable analysis for different scenarios
场景 输入指标松弛变量 输出指标松弛变量 FD1 PD SD SR HR R SampEn FD2 A 0.0 0.0 0.00 0 0 0.0 0.0 0.0 B 0.0 0.0 0.10 2 5 0.0 0.0 0.0 C 0.1 0.1 0.20 4 10 0.1 0.1 0.1 D 0.0 0.0 0.05 1 3 0.0 0.0 0.0
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