A Method for Assessing the Risks of Freeway Segments with Combined Horizontal and Vertical Curves
-
摘要: 高速公路平纵曲线组合路段常出现单一平曲线和竖曲线要素满足规范，但二者相结合后存在安全隐患的情况。为评估这类组合路段的交通风险、提升组合路段安全性，综合运用可拓云理论与理想点法，提出了基于可拓云模型的交通风险评估方法。基于已有事故数据和文献，从驾驶员、道路、交通环境以及其他因素的角度出发，构建了包含15个指标的交通风险评估指标体系，并将每个指标划分为5个风险等级；利用层次分析法和熵权法确定各评估指标主、客观权重后，再通过理想点法确定各评估指标组合权重；参照公路路线设计规范及相关文献，考虑定性指标的边界模糊性划分各评估指标的风险等级，并按照等比原则实现定性指标的定量化描述；构造可拓云模型云隶属度矩阵，计算综合评判向量，最后根据最大隶属度原则确定路段风险等级。以云南省3段高速公路路段作为分析案例，利用基于可拓云模型的交通风险评估方法计算了各路段风险等级，并识别了各路段的危险性指标。结果表明：该方法与传统基于模糊综合评价法相比，评估结果相同，但信息更丰富，其综合评判模糊等级特征值的期望Exr反映了路段的安全程度；Y路段的Exr高于C路段，表明Y路段比C路段更安全；3段路段的评估结果的置信度因子θ均小于0.05，表明结果可信度较高，验证了该方法在交通风险评估过程中的适用性。Abstract: Freeway segments with combined horizontal and vertical curves might not satisfy the safety specifications of Highway Alignment Design(hereafter referred as HAD)even though each of them does so. To assess the risks of such road sections and improve road safety, a safety assessment method is proposed by using the extendable cloud theory and the ideal point method(IPM). Firstly, a road safety evaluation system is developed, which includes 15 indicators from the following five perspectives: drivers, roads, traffic, environment, and others, and each indicator is coded as one of five levels. Secondly, subjective and objective weights of the indicators are determined by the analytic hierarchy process(AHP)and entropy weight method(EWM), respectively, which are combined into one using the ideal point method.Thirdly, the risk levels of each indicator are classified based on the specifications, considering the fuzz boundaries of the qualitative indicators, and the qualitative indicators are quantified based on the principle of equal ratio. Finally, the membership evaluation matrix is developed, a comprehensive assessment vector is calculated, and the level of risk of road section is determined by the maximum membership principle. To demonstrate the proposed method, three cases from the Yunnan Province are used, and the results show that the proposed method not only provides compliant outcomes with the traditional fuzzy comprehensive assessment method but also offers more information. Specifically, the expected value of fuzzy grade eigenvalue for comprehensive assessment, i.e. Exr, reflects the safety level of the road sections; Confidence Factor θ reveals the reliability level of the result. In the studied cases, Exr of Section Y is higher than that of Section C, showing that Section Y is safer than Section C; Confidence Factor θ are all under 0.05, showing that the results are reliable. These results reveal the potential of the proposed method for road safety assessment.
-
图 4 不同平纵曲线组合形式眨眼频率变化图
Figure 4. Variation diagram of blink frequency in different horizontal and vertical line combinations
图 5 不同驾驶员心率变异性变化图
Figure 5. Variation chart of heart rate variability among different drivers
图 6 Y路段视觉特性风险等级云图
Figure 6. Cloud map of risk level of visual characteristics of Section Y
图 7 各路段评估指标隶属风险等级图
Figure 7. Assessment indicators of each road section belong to the risk level map
表 1 各评估指标风险等级划分依据
Table 1. Basis for the classification of risk levels of each assessment indicator
下载: 导出CSV
表 2 风险评估指标等级界限划分
Table 2. Classification of risk assessment indicators levels
评估指标 范围 分级 E级 D级 C级 B级 A级 视觉特性（眨眼频率）P1/（次/10 s）（转化为评分） (0, 100] (0, 20] (20, 40] (40, 60] (60, 80] (80, 100] 心理、生理特性(HRV) P2/ms2(转化为评分） (0, 100] (0, 20] (20, 40] (40, 60] (60, 80] (80, 100] 平曲线最小半P3/103m (0, 10] (0, 1] (1, 2] (2, 4] (4, 5] (5, 10] 凹形竖曲线最小半径P4/103m (0，） (0, 2] (2, 4] (4, 6] (6, 25] (25，） 凹形竖曲线最小半径/103m(转化为评分） (0, 100] (0, 20] (20, 40] (40, 60] (60, 80] (80, 100] 凸形竖曲线最小半径P5/103m (0，） (0, 3] (3, 11] (11, 17] (17, 30] (30，) 凸形竖曲线最小半径/103m(转化为评分） (0, 100] (0, 20] (20, 40] (40, 60] (60, 80] (80, 100] 最大纵坡（绝对值）P6/（°） (0, 6] (4, 6] (3, 4] (2, 3] (1, 2] (0, 1] 最大坡度差P7/(°) (0, 10] (8, 10] (6, 8] (4, 6] (2, 4] (0, 2] 最大坡长P8/m (0, 1 100] (900, 1 100] (800, 900] (700, 800] (600, 700] (0, 600] 竖曲线最短长度P9/m (0，） (0, 70) (70, 100] (100, 250] (250, 300] (300, ^) 竖曲线最短长度(m)(转化为评分） (0, 100] (0, 20] (20, 40] (40, 60] (60, 80] (80, 100] 最小合成坡度P10/% (0, 8] (0, 0.3] (0.3, 0.5] (0.5, 1] (1, 2] (2, 8] 最小平纵指标均衡性(竖/平）P11 (0，） (0, 5] (5, 10] (10, 15] (15, 20] (20，) 最小平纵指标均衡性(竖/平)（转化为评分）P11 (0, 100] (0, 20] (20, 40] (40, 60] (60, 80] (80, 100] 最小变坡点与平曲线的曲中错位率P12/% (0, 100] (40, 100] (30, 40] (20, 30] (10, 20] (0, 10] 大车比例p13/% (0, 100] (80, 100] (60, 80] (40, 60] (20, 40] (0, 20] 地区不良气候天数P14/% (0, 100] (80, 100] (60, 80] (40, 60] (20, 40] (0, 20] 超载率P15/% (0, 100] (80, 100] (60, 80] (40, 60] (20, 40] (0, 20]
下载: 导出CSV
表 3 标准正态云模型
Table 3. Standard normal cloud model
评估指标 分级 E级 D级 C级 B级 A级 视觉特性（眨眼频率）P1 (10, 3.33, 0.333) (30,3.33,0.333) (50, 3.33, 0.333) (70, 3.33, 0.333) (90, 3.33, 0.333) 心理、生理特性(HRV) P2 (10, 3.33, 0.333) (30, 3.33, 0.333) (50, 3.33, 0.333) (70, 3.33, 0.333) (90, 3.33, 0.333) 平曲线最小半径P3 (0.5, 0.17, 0.017) (1.5, 0.17, 0.017) (3, 0.33, 0.033) (4.5, 0.17, 0.017) (7.5, 0.83, 0.083) 凹形竖曲线最小半径P4 (10, 3.33, 0.333) (30, 3.33, 0.333) (50, 3.33, 0.333) (70, 3.33, 0.333) (90, 3.33, 0.333) 凸形竖曲线最小半径P5 (10, 3.33, 0.333) (30, 3.33, 0.333) (50, 3.33, 0.333) (70, 3.33, 0.333) (90, 3.33, 0.333) 最大纵坡P6 (5, 0.33, 0.033) (3.5, 0.17, 0.017) (2.5, 0.17, 0.017) (1.5, 0.17, 0.017) (0.5, 0.17, 0.017) 最大坡度差P7 (9, 0.33, 0.033) (7, 0.33, 0.033) (5, 0.33, 0.033) (3, 0.33, 0.033) (1, 0.33, 0.033) 最大坡长P8 (1 000, 33.33, 3.333) (850, 16.67, 1.667) (750, 16.67, 1.667) (650, 16.67, 1.667) (300, 100, 10.000) 竖曲线最短长度P9 (10, 3.33, 0.333) (30, 3.33, 0.333) (50, 3.33, 0.333) (70, 3.33, 0.333) (90, 3.33, 0.333) 最小合成坡度P10 (0.15, 0.05, 0.005) (0.4, 0.03, 0.003) (0.75, 0.08, 0.008) (1.5, 0.17, 0.017) (5, 1, 0.100) 最小平纵指标均衡性(竖/平）P11 (10, 3.33, 0.333) (30, 3.33, 0.333) (50, 3.33, 0.333) (70, 3.33, 0.333) (90, 3.33, 0.333) 最小变坡点与平曲线的曲中错位率P12 (70, 10, 1.000) (35, 1.67, 0.167) (25, 1.67, 0.167) (15, 1.67, 0.167) (5, 1.67, 0.167) 大车比例P13 (90, 3.33, 0.333) (70, 3.33, 0.333) (50, 3.33, 0.333) (30, 3.33, 0.333) (10, 3.33, 0.333) 地区不良气候天数P14 (90, 3.33, 0.333) (70, 3.33, 0.333) (50, 3.33, 0.333) (30, 3.33, 0.333) (10, 3.33, 0.333) 超载率P15 (90, 3.33, 0.333) (70,3.33,0.333) (50, 3.33, 0.333) (30, 3.33, 0.333) (10, 3.33, 0.333)
下载: 导出CSV
表 4 试验路段概况
Table 4. Overview of test section
路段 路段长度/km 设计速度/(km/h) 汕昆高速
(阳宗收费站一草甸收费站）8.85 80 杭瑞高速
(彩云收费站一恐龙谷收费站）21.7 80/100 杭瑞高速
(旧县收费站一马龙收费站）18.2 80/100
下载: 导出CSV
表 5 线形单元划分
Table 5. Division of linear elements
线形单元编号 横断面 纵断面 1 直线 直坡 2 直线 坚曲线 3 缓和曲线 直坡 4 缓和曲线 坚曲线 5 圆曲线 直坡 6 圆曲线 坚曲线 7 直线 8 缓和曲线 9 圆曲线
下载: 导出CSV
表 6 驾驶员信息
Table 6. Driver information
编号 年龄/岁 性别 驾龄/年 1 27 男 5 2 30 女 4 3 34 男 11 4 38 男 14 5 31 女 8
下载: 导出CSV
表 7 路段概况及相关数据
Table 7. Road profile and related data
路段 设计速度/ (km/h) 桩号范围 路线长度/km CHVC占比/% 事故统计时间 事故绝对数 Y 80 K24+244—K33+094 8.85 37.4 2015-01—37 4 2020-12 479 C 80 K94+172—K105+852 11.68 42.3 2015-01—42.32020-12 627 H 80 K28+478—K37+268 8.79 44.1 2015-01—44.12020-12 642
下载: 导出CSV
表 8 各路段评估指标值
Table 8. Assessment indicators value of each road section
风险源 评估指标 Y路段 C路段 H路段 驾驶员因素K1 视觉特性（眨眼频率）P1 /(次/10 s) 2.15 2.05 1.95 视觉特性（眨眼频率）P1(转化为评分） 82 78 74 心理、生理特性（HRV) P2/ms2 1 237 1 190 1 126 心理、生理特性(HRV) P2 (转化为评分） 78 75 71 道路条件因素K2 平曲线最小半径P3 /103 m 0.83 1.49 1.16 凹形竖曲线最小半径P4 /103 m 7.58 5.43 5.7 凹形竖曲线最小半径（103 m）P4（转化为评分） 61.66 54.3 57 凸形竖曲线最小半径P5 /103 m 15.05 10.4 11.06 凸形竖曲线最小半径P5/103 m(转化为评分） 53.5 38.5 40.2 最大纵坡(绝对值）P6/(°) 3 3 3.5 最大坡度差P7/(°) 3.5 3 4 最大坡长P8 /m 650 730 680 竖曲线最短长度P9 /m 134.6 113 96.57 竖曲线最短长度P9/m(转化为评分） 44.61 41.73 37.71 最小合成坡度P10 /% 1.2 0.8 0.9 最小平纵指标均衡性（竖/平）P11 13.17 14.83 14.26 最小平纵指标均衡性（竖/平）P11（转化为评分） 52.68 59.32 57.04 最小变坡点与平曲线的曲中错位率P12 /% 1.1 3.9 2.1 交通环境因素K3 大车比例P13 /% 16.38 21.24 18.76 其他因素K4 地区不良气候天数P14/% 14.7 18.5 13.3 超载率P15/% 4.2 6.7 5.6
下载: 导出CSV
表 9 专家可信度评判标准
Table 9. Expert credibility assessment standard
评判因素 权重l 级别 分值q 职称 2 正高级 0.8 副高级 0.6 中级 0.4 学位 1 博士 0.8 硕士 0.6 学士 0.4 工龄/年 4 > 30 0.8 15~30 0.6 < 15 0.4 经历 3 有类似评估经历 0.8 无类似评估经历 0.4
下载: 导出CSV
表 10 各评估指标权重
Table 10. Weighting of each assessment indicator
评估指标 主观权重wj 客观权重wj 组合权重Kj 排序 P1 0.027 2 0.065 0 0.048 4 11 P2 0.027 2 0.052 1 0.040 3 12 P3 0.091 7 0.112 6 0.099 6 3 P4 0.072 6 0.071 7 0.070 0 9 P5 0.090 9 0.080 6 0.083 3 4 P6 0.092 2 0.138 4 0.114 1 1 P7 0.096 3 0.118 2 0.104 6 2 P8 0.076 0 0.092 0 0.081 9 6 P9 0.066 9 0.077 5 0.070 2 8 P10 0.096 4 0.069 2 0.081 4 7 P11 0.104 6 0.060 1 0.082 7 5 P12 0.055 9 0.006 6 0.038 6 13 P13 0.067 8 0.023 2 0.049 2 10 P14 0.016 6 0.027 4 0.022 0 14 P15 0.019 1 0.005 3 0.013 6 15
下载: 导出CSV
表 11 Y路段各评估指标隶属度评判矩阵
Table 11. Assessment matrix of membership degree of each assessment indicator in Section Y
Y路段 E级 D级 C级 B级 A级 P1 0 0 1.07×10-20 0.001 6 0.056 5 P2 0 0 5.12×10-16 0.056 5 0.001 6 P3 0.175 0 7.57×10-4 9.60×10-10 0 0 P4 0 2.82×10-20 0.0022 0.044 0 2.17×10-16 P5 0 1.70×10-11 0.576 9 4.90×10-6 1.04×10-26 P6 2.19×10-8 0.0183 0.0183 2.28×10-16 0 P7 0 3.48×10-24 4.91×10-5 0.332 1 1.07×10-12 P8 0 0 1.56×10-8 1 2.19×10-3 P9 4.34×10-24 6.87×10-5 0.271 3 2.67×10-13 0 P10 0 0 1.43×10-6 0.236 8 8.06×10-4 P11 0 9.29×10-11 0.7243 1.41×10-6 6.85×10-28 P12 0 0 0 1.19×10-15 0.066 9 P13 0 0 9.02×10-23 2.41×10-4 0.1607 P14 0 0 4.98×10-25 2.72×10-5 0.3708 P15 0 0 0 1.04×10-13 0.2208
下载: 导出CSV
表 12 各路段交通风险综合评判向量
Table 12. Comprehensive assessment vector of traffic risk for each road section
路段 E级 D级 C级 B级 A级 最终风险等级 Y 0.0174 0.002 2 0.129 3 0.141 3 0.024 7 B级 C 1.78x10-8 0.105 0 0.1466 0.122 6 0.040 6 C级 H 0.0009 0.1354 0.037 4 0.041 6 0.031 8 D级
下载: 导出CSV
表 13 各路段评估结果对比
Table 13. Comparison of assessment results of each road section
路段 期望值Exr 置信度因子θ 危险指标 较危险指标 Y 3.488 0 0.014 P3 无 C 3.238 4 0.031 无 P3，P5 H 2.870 6 0.027 无 P3，P6，P9
下载: 导出CSV
表 14 不同方法评估结果对比
Table 14. Comparison of assessment results of different methods
路段 可拓云模型 模糊综合评价法 Y B级 B级 C C级 C级 H D级 D级
下载: 导出CSV
-
[1] 戢晓峰, 吴亚欣, 郝京京, 等. 平纵组合路段事故严重程度致因辨识模型[J]. 交通运输系统工程与信息, 2020, 20(6): 197-204. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202006026.htmJI X F, WU Y X, HAO J J, et al. Severity factors analysis model of traffic accident on combined horizontal and vertical alignments[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(6): 197-204. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202006026.htm [2] 胡立伟, 薛刚, 李林育, 等. 高原地质及气象环境下公路交通风险致因耦合分析[J]. 中国公路学报, 2018, 31(1): 110-119. doi: 10.3969/j.issn.1001-7372.2018.01.013HU L W, XUE G, LI L Y, et al. Analysis of coupling of highway traffic risks in geological and meteorological environment of plateau regions[J]. China Journal of Highway and Transport, 2018, 31(1): 110-119. (in Chinese) doi: 10.3969/j.issn.1001-7372.2018.01.013 [3] ALIAKSEI L, ASE S, CHRISTER H. Evaluation of traffic safety, based on micro-level behavioural data: Theoretical framework and first implementation[J]. Accident Analysis & Prevention, 2010, 42(6): 1637-1646. [4] 张存保, 彭汉辉, 张珊, 等. 雾天高速公路实时交通安全状态评价方法[J]. 中国安全科学学报, 2017, 27(4): 110-115. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201704021.htmZHANG C B, PENG H H, ZHANG S, et al. Real-time traffic safety evaluation method for freeway in fog[J]. China Safety Science Journal, 2017, 27(4): 110-115. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201704021.htm [5] 孔令铮. 交通事故致因中的人为因素分析[J]. 中国安全科学学报, 2013, 23(1): 28-34. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201301006.htmKONG L Z. Human factors in causation of traffic accidents[J]. China Safety Science Journal, 2013, 23(1): 28-34. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201301006.htm [6] 温惠英, 薛刚. 山区公路交通事故多要素风险综合分析方法: 以山区雨雾天气为例[J]. 中国安全科学学报, 2019, 29 (9): 161-166. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201909030.htmWEN H Y, XUE G. Comprehensive analysis method of multi-factor risks for highway traffic accidents in mountain areas: taking rain and fog weather for example[J]. China Safety Science Journal, 2019, 29(9): 161-166. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201909030.htm [7] ABDEL-ATY M, EKRAM A A, Huang H, et al. A study on crashes related to visibility obstruction due to fog and smoke[J]. Accident Analysis & Prevention, 2011, 43(5): 1730-1737. [8] PENMETSA P, PULUGURTHA S S. Modeling crash injury severity by road feature to improve safety[J]. Traffic Injury Prevention, 2018, 19(1): 102-109. doi: 10.1080/15389588.2017.1335396 [9] MORENO A T, LIORCA C, GARCIA A. Operational impact of horizontal and vertical alignment of two-lane highways[J]. Transportation Research Procedia, 2016(15): 319-330. [10] 丰明洁, 王婷, 王雪松, 等. 山区高速公路平纵组合路段设计安全评估[J]. 北京工业大学学报, 2018, 44(6): 919-925. https://www.cnki.com.cn/Article/CJFDTOTAL-BJGD201806016.htmFENG M J, WANG T, WANG X S, et al. Safety evaluation of combined horizontal and vertical alignments on mountainous freeways[J]. Journal of Beijing University of Technology, 2018, 44(6): 919-925. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BJGD201806016.htm [11] 高建平, 窦浩然, 何云勇, 等. 基于驾驶员人因的高速公路长大连续下坡路段线形组合设计方法[J]. 公路, 2021, 66 (11): 7-13. doi: 10.12361/2661-3514-03-11-07GAO J P, DOU H R, HE Y Y, et al. Research on the alignment combinaton method of long downhill section in mountainous expressway based on driver's human factor[J]. Highway, 2021, 66(11): 7-13. (in Chinese) doi: 10.12361/2661-3514-03-11-07 [12] 岳雷, 王慧, 杜豫川, 等. 基于安全性分析的山区公路弯坡组合路段设计指标研究[J]. 工程力学, 2019, 36(11): 158-167. https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX201911019.htmYUE L, WANG H, DU Y C, et al. Mountain highway design indexs of assemble section of flat and vertical curve based on safety analysis[J]. Engineering Mechanics, 2019, 36(11): 158-167. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX201911019.htm [13] 陈亦新, 王雪松. 山区高速公路组合线形路段车道偏移行为[J]. 中国公路学报, 2018, 31(4): 98-104. doi: 10.3969/j.issn.1001-7372.2018.04.012CHEN Y X, WANG X S. Effects of combined alignments of mountainous freeways on lane departure[J]. China Journal of Highway and Transport, 2018, 31(4): 98-104. (in Chinese) doi: 10.3969/j.issn.1001-7372.2018.04.012 [14] 王峰, 刘波峰, 王丹璐. 基于物元-可拓理论的城市轨道交通预装式变电站状态评估[J]. 铁道科学与工程学报, 2020, 17(2): 477-484. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202002026.htmWANG F, LIU B F, WANG D L. Status assessment for the urban rail transit preloaded substations based on matter-element extensible theory[J]. Journal of Railway Science and Engineering, 2020, 17(2): 477-484. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202002026.htm [15] 杨帆. 高速公路纵面线形指标及其组合与交通安全关系研究[D]. 西安: 长安大学, 2018.YANG F. Study on the relationship between the vertical geometric alignments and traffic safety of highway[D]. Xi'an: Chang'an University, 2018. (in Chinese) [16] 谭泽斌. 基于可拓云模型的隧道交通安全评价研究[D]. 武汉: 华中科技大学, 2019.TAN Z B. A thesis submitted in partial fulfillment of the requirements for the degree of master of engineering[D]. Wuhan: Huazhong University of Science and Technology, 2019. (in Chinese) [17] 郭羽熙, 袁伟, 陈运星. 城市道路环境中驾驶人视觉行为与工作负荷的相关性[J]. 长安大学学报(自然科学版), 2020, 40(5): 97-104. https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL202005010.htmGUO Y X, YUAN W, CHEN Y X. Correlation between driver's visual behavior and workload in urban road environment[J]. Journal of Chang'an University(Natural Science Edition), 2020, 40(5): 97-104. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL202005010.htm [18] 李显生, 孟凡淞, 郑雪莲, 等. 基于心率变异性的应激场景下驾驶人心理特性分析[J]. 上海交通大学学报, 2018, 52 (2): 163-168. https://www.cnki.com.cn/Article/CJFDTOTAL-SHJT201802007.htmLI X S, MENG F S, ZHENG X L, et al. Psychological characteristics of drivers in the stress scene based on heart rate variability[J]. Journal of Shanghai Jiaotong University, 2018, 52(2): 163-168. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SHJT201802007.htm [19] WU H W, ZHEN J, ZHANG J. Urban rail transit operation safety evaluation based on an improved critic method and cloud model[J]. Journal of Rail Transport Planning & Management, 2020(16): 100206. [20] 李林波, 郭晓凡, 傅佳楠, 等. 基于云模型的城市轨道交通乘客满意度评价[J]. 同济大学学报, 2019, 47(3): 378-385. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201903011.htmLI L B, GUO X F, FU J N, et al. Evaluation approach of passenger satisfaction for urban rail transit based on cloud model[J]. Journal of Tongji University, 2019, 47(3): 378-385. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201903011.htm [21] 薛黎明, 刘保康, 郑志学, 等. 基于理想点-可拓云模型的煤炭资源可持续力评价[J]. 自然资源学报, 2018, 33(9): 1657-1665. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZX201809015.htmXUE L M, LIU B K, ZHENG Z X, et al. Evaluation of sustainable capacity of coal resources based on extension cloud theory and ideal point method[J]. Journal of Natural Resources, 2018, 33(9): 1657-1665. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZX201809015.htm [22] NICKEI P, NACHREINER F. Sensitivity and diagnosticity of the 0.1-Hz component of heart rate variability as an indicator of mental workload[J]. Human Factors, 2003, 45(4): 575-590. [23] 盛彬. 基于信息融合的驾驶危险场景分析识别研究[D]. 哈尔滨: 哈尔滨工业大学, 2019.SHENG B. Research on analysis and identification of driving dangerous scenarios based on information fusion[D]. Harbin: Harbin Institute of Technology, 2019. (in Chinese) [24] 张锦, 徐君翔. 基于可拓理论的艰险山区铁路施工风险预警[J]. 安全与环境学报, 2020, 20(3): 824-831. https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ202003005.htmZHANG J, XU J X. Early warning against the construction risks of high-speed railway in hard & dangerous mountainous areas based on the extension theory[J]. Journal of Safety and Environment, 2020, 20(3): 824-831. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ202003005.htm [25] 王晓玲, 戴林瀚, 吕鹏, 等. 基于DSR-可拓云的渗流安全综合评价研究[J]. 天津大学学报(自然科学与工程技术版), 2019, 52(1): 52-61. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDX201901008.htmWANG X L, DAI L H, LYU P, et al. Study on comprehensive evaluation model of seepage safety based on DSR-extension cloud[J]. Journal of Tianjin University(Science and Technology), 2019, 52(1): 52-61. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDX201901008.htm -
点击查看大图
图(7) / 表(14)
计量
- 文章访问数: 1080
- HTML全文浏览量: 463
- PDF下载量: 163
- 被引次数: 0