An Analysis of Influential Factors of Crashes at Tunnels and Open Sections of Mountainous Freeways
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摘要: 高速公路隧道构造特殊且通行环境复杂，因而通常事故多发。为探究高速公路隧道路段与开放路段事故影响因素和严重程度致因机理的差异，采集沪昆高速邵怀段2011—2016年期间1 537起事故为研究样本；以事故发生路段为响应变量构建逻辑回归模型，解释各种风险因素对事故发生路段倾向性的影响差异；分别针对隧道路段与开放路段建立模型研究事故伤害严重程度的影响因素。建立二元Logit回归模型分析事故的发生倾向性和2类路段的事故严重程度的影响因素；采用随机参数Logit模型以反映异质性条件对参数的影响。统计表明：与疲劳驾驶、未保持安全距离相关的事故发生在隧道路段的概率更高，其事故发生概率分别是开放路段的2.373和2.482倍；与隧道路段事故严重程度正相关的因素包括下坡（坡度2%以上）、夏季和超速行驶，其中下坡（坡度2%以上）段的严重事故发生的概率为上坡（坡度2%以上）的3.397倍，夏季的严重事故发生概率为秋季的3.951倍，超速行驶相关的严重事故发生概率为其他不当驾驶行为的4.242倍；与开放路段事故严重程度正相关的因素包括超速行驶和疲劳驾驶，其中超速行驶相关的严重事故概率是其他不当驾驶行为的2.713倍，疲劳驾驶相关的严重事故概率是其他不当驾驶行为的4.802倍。研究表明，山区高速公路隧道路段与开放路段的事故发生概率及其严重程度的影响因素存在一定的差异性，研究结论可为山区高速公路差异管理方案制定提供依据。Abstract: Freeway tunnels tend to have higher accident rates, due to their special engineering structure and complex traffic environments, compared to regular segments. In order to study the differences in mechanisms and factors influencing severity of crashes in tunnels and regular open sections on freeways, a total of 1 537 crashes taking place on Shaohuai Freeway from 2011 to 2016 are collected for the analysis. A binary Logit model considering the heterogeneity is used to explain the impacts of various risk factors on the likelihood of the locations of traffic crashes and to investigate the factors influencing severity of crashes taking place at tunnel and open sections. Statistical analysis results show that crashes associated with drowsy driving and unsafe following distance are more likely to occur in the tunnel sections, and the crash probability is 2.373 and 2.482 times higher than that in the open sections, respectively. In the tunnel sections, downhill (slope more than 2%), summer, and speeding are positively correlated to the likelihood of injury crashes, and the probability that crashes take place at downhill (slope more than 2%) sections is 3.397 higher than uphill (slope more than 2%) sections in resulting in serious accidents. It is also found that the probability of serious crashes taking place in the summer is 3.951 higher than that in the autumn; and the probability of the speeding behaviors is 4.242 higher than other inappropriate behavior. In the open sections, speeding and fatigue driving are likely to be associated with injury crashes, and the probability that speeding results in an injury crash is 2.713 higher than other inappropriate behavior. It is also found that the probability that the fatigue driving leading to an injury crash is 4.802 higher than other inappropriate behavior. The above results show that the factors influencing the crash propensity and severity at the two types of sections are different. The conclusions of this paper can be used to formulate road safety improvement plans over tunnel and open section segments of freeways.
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Key words:
- road safety /
- crash severity /
- freeway tunnel /
- influential factors /
- heterogeneity /
- random parameter Logit model
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表 1 涉及隧道分段及事故特征的文献
Table 1. Studies on zone division and crash characteristics of tunnels
研究对象 隧道分段 主要结论 挪威道路隧道[12] 4段：接近段（隧道洞口外50 m）、入口段（洞口后50 m）、转换段（紧挨入口段的100 m）、中间段(隧道内其他段，下同) 隧道内部比开放路段事故的伤害严重性高；隧道接近段事故频率最高，其次为入口段；长隧道事故发生率比短隧道低 中国高速公路隧道[13] 4段: 接近段（隧道洞口外100 m）、入口段（洞口后100 m）、转换段（紧挨入口段的300 m）、中间段 隧道事故比开放路段事故的伤害严重性高；隧道内部转换段事故频率最高；1月是隧道事故高发月份；09：00—10：00，15：00—17：00是隧道事故的高发时段 新加坡城市快速路隧道[14] 3段：外部转换段（隧道洞口外250 m）；内部转换段（洞口后250 m）；中间段 隧道中间段事故比转换段事故的伤害严重程度高；外部转化段事故发率最高；隧道进口事故率高于隧道出口事故率 中国山区高速公路隧道群[15] 5段：接近段（第1座隧道洞口外400 m）、内部转换段（洞口后300 m）、连接段（相邻隧道连接区段）、中间段、隧道群出口段（出隧道后600 m） 隧道群路段事故率高于单个隧道路段；连接段事故率最高，尤其是追尾事故率高于其他区段；照明条件对隧道出入口事故风险影响显著，“黑洞效应”事故风险高于“明洞效应” 
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表 2 变量描述性统计（若事故发生时该因素存在，赋值为1，否则赋值为0）
Table 2. Descriptive statistics of variables included in the models (1 if the variable statement is true; 0 otherwise)
变量 隧道计数/% 开放计数/% 变量 隧道计数/% 开放计数/% 伤害等级 车辆类型 死伤 19(3.9) 76(7.3) 小型机动车* 380(77.6) 740(70.7) 仅财产损失* 471(96.1) 971(92.7) 中大型客车 31(6.3) 71(6.8) 坡度 货车 79(16.1) 236(22.5) 下坡(坡度2%以上） 36(7.3) 313(29.9) 事故认定原因 下坡(坡度0~2%) 147(30.0) 265(25.3) 超速行驶 8(1.6) 74(7.1) 上坡(坡度0~2%) 166(33.9) 201(19.2) 疲劳驾驶 18(3.7) 42(4.0) 上坡(坡度2%以上)* 141(28.8) 268(25.6) 违法变道 32(6.5) 80(7.6) 曲线半径/m 未保持安全距离 328(66.8) 440(42.1) 0~1 000 71(14.5) 219(20.9) 转向 & 刹车不当 13(2.6) 61(5.8) 1 000~4 000 139(28.4) 587(56.1) 其他不当行为* 92(18.7) 349(33.4) ∞(直线路段)* 280(57.1) 241(23.0) 驾驶人年龄/岁 季节 20 ~29 92(18.8) 220(21.0) 春季 112(23.1) 186(17.7) 30 ~39* 177(36.1) 392(37.4) 夏季 27(5.5) 121(11.5) 40 ~49 174(35.5) 367(35.1) 秋季* 51(10.5) 145(13.8) > 50 47(9.6) 68(6.5) 冬季 294(60.7) 601(57.1) 驾驶人驾龄/年 时间 0~4 196(40) 470(44.9) 白天* 378(77.1) 563(53.8) 5~9 161(32.9) 283(27.0) 夜间 112(22.9) 484(46.2) 10 ~14* 79(16.1) 158(15.1) 天气 > 15 54(11.0) 136(13.0) 晴天* 217(44.3) 417(39.8) 阴天 142(29.0) 229(21.9) 雨/雪/雾 131(26.7) 587(56.1) 注：*代表参考变量。
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表 3 路段类型*事故严重程度卡方检验结果
Table 3. Chi-square test of road section * crash severity
指标 χ2 精确显著性(双侧） 精确显著性(单侧） 皮尔逊卡方 6.581 0.012 0.006 似然比 7.123 0.009 0.006 线性关联 6.577 0.012 0.006
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表 4 拟合优度检验表
Table 4. Goodness-of-fit measures for models
模型 样本量 对数似然值 似然比 AIC 事故倾向性 隧道VS开放 二元Logit模型 1 537 -755.352 0.215 1 534.7 随机参数Logit模型 1 537 -738.468 0.224 1 510.9 事故严重程度 隧道路段 二元Logit模型 490 -67.619 0.159 143.2 随机参数Logit模型开放路段 490 -65.837 0.001 145.7 二元Logit模型 1 047 -209.157 0.232 428.3 随机参数Logit模型 1 047 -205.127 0.019 428.3 均值异质性模型 1 047 -206.758 0.011 427.5
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表 5 2种路段事故发生倾向性影响因素研究模型结果
Table 5. Estimate results for crash tendency models
变量 参数估计户 p > |Z| 优势比 常数项 -0.377 0.024 0.686 下坡(坡度2%以上） -1.330 < 0.001 0.264 上坡(坡度0~2%) -1.329 < 0.001 0.265 上坡坡度(0~2%)的标准差 0.163 < 0.001 曲线半径(0~1 000 m) -1.430 < 0.001 0.239 曲线半径(> 1 000~4 000 m) -1.341 < 0.001 0.262 曲线半径(> 1 000~4 000 m)的标准差 0.155 < 0.001 夏季 -0.779 0.002 0.459 夜间 -0.953 < 0.001 0.386 夜间的标准差 0.209 < 0.001 雨/雾/雪 -0.484 0.001 0.616 雨/雾/雪的标准差 0.139 < 0.001 超速行驶 -1.038 0.025 0.354 超速行驶的标准差 1.825 0.001 疲劳驾驶 0.864 0.011 2.373 未保持安全距离 0.909 < 0.001 2.482
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表 6 隧道路段事故严重程度影响因素模型结果
Table 6. Estimate results in tunnel for crash severity models
变量 隧道路段 开放路段 估计值 P > |Z| 优势比 估计值 P > |Z| 优势比 常数项 -2.171 0.000 0.114 -1.390 0.000 下坡(2%以上） 1.223 0.015 3.397 下坡(2%以上）的标准差 0.501 0.015 夏季 1.374 0.004 3.951 与固定物碰撞 -1.218 0.000 0.296 超速行驶 1.445 0.067 4.242 0.998 0.028 2.713 超速行驶的标准差 0.791 0.067 0.453 0.028 疲劳驾驶 1.569 0.000 4.802 未与保持适当的安全距离 -1.073 0.011 0.342 -2.117 0.000 0.120 随机参数均值异质性 超速行驶：货车 -1.185 0.082
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