A Comparative Analysis of Heterogeneous Effects of Various Factors on Accident Severity at Sharp Curve Sections of Mountainous Highway
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摘要: 为分析影响山区公路小半径路段典型事故的严重程度的相关因素及其异质性效应，基于某山区双车道公路1 067起交通事故数据，从驾驶员、车辆、道路和环境4个方面选取15个潜在特征变量，采用二项Logit模型和随机参数二项Logit模型，分别构建小半径弯道路段上追尾碰撞、正面碰撞和侧面碰撞3类典型事故的严重度分析模型，分析3类典型事故严重度的显著影响因素，并采用边际弹性系数量化分析影响因素的作用强度。结果表明，小半径弯道路段上不同形态事故的严重度影响因素存在明显差异：①追尾碰撞严重度的显著影响因素依次为摩托车、夜间、弯道转角、驾驶员年龄、季节，摩托车和冬季分别是服从(2.716.1.5642)和(-1.495，2.1162)正态分布的异质性影响因素，导致发生伤亡事故的概率为95.72%和23.58%；②正面碰撞严重度的显著影响因素依次为货车、摩托车、驾驶员超车、弯道转角和弯道长度，货车导致其伤亡事故概率增加108.8%，摩托车和弯道长度分别是服从(6.941，9.9012)和(-0.004，0.0032)正态分布的异质性影响因素，导致发生伤亡事故的概率为76.11%和9.18%；③侧面碰撞严重度的显著影响因素依次为摩托车、驾驶员年龄及弯道有接入口，摩托车和接入口分别是服从(5.211，5.1112)和(-1.408，2.1462)正态分布的异质性影响因素，导致发生伤亡事故的概率为88.87%和25.47%。④与传统二项Logit模型相比，追尾碰撞、正面碰撞和侧面碰撞的随机参数二项Logit模型的拟合优度分别提高了2.85%，4.15%，6.76%，且定量捕捉了异质性影响因素，更适用于事故严重度的精细化分析。Abstract: To identify contributing factors and their heterogeneity effects onto accident severity at sharp curve sections of mountainous highway, fifteen potential factors are selected from the following areas including driver, vehicle, road, and environment conditions based on the data from 1 067 accidents on a two-lane highway in mountain areas. Then, a binary Logit model, and a random parameters binary Logit model are used to analyze severity of three typical types of accidents including rear-end, head-on, and side collision. Results show that there are significant differences in the effect of impact factors on crash severity of three types of accidents at sharp curve sections as follows: ①For rear-end collisions, the significant variables of crash severity are motorcycle, night, cornering, age of drivers, and different seasons. Motorcycle and winter are heterogeneous influence factors obeying a normal distribution with a mean value of 2.716 and -1.495, a variance of 1.564 and 2.116. The probability of resulting in a casualty accident is 95.72% and 23.58%, respectively. ②For head-on collisions, the significant variables of crash severity are truck, motorcycle, overtaking of drivers, curve corners, and curve lengths in turn. The probability of casualty accidents with the truck increases by 108.8%. The motorcycle and curve lengths are heterogeneous influencing factors obeying a normal distribution, with a mean value of 6.941 and -0.004, a variance of 9.901 and 0.003. Consequently, the probability of casualty accident is 76.11% and 9.18%, respectively. ③For side collisions, the significant variables of crash severity are motorcycle, age of drivers, and corner with entrance in turn. The motorcycle and corner with entrance are heterogeneous influencing factors obeying a normal distribution, with a mean value of 5.211 and -1.408, and a variance of 5.111 and 2.146. Consequently, the probability of casualty accident is 88.87% and 25.47%, respectively. ④Compared with the traditional binomial Logit models, the accuracy of the random parameter binary Logit models for predicting crash severity of the rear-end, head-on, and side collision are increased by 2.85%, 4.15%, and 6.76%, respectively. With the proposed model, the heterogeneous effects of several factors can be quantitatively captured, and therefore, it can be used for improved severity analysis of road accidents.
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表 1 自变量描述性统计信息
Table 1. Descriptive statistics of independent variables
变量 符号 描述 编码 一般事故(n =676) 伤亡事故（n =369) 频数(均值a） 占比/% (标准差b） 频数(均值a） 占比/%(标准差b） 驾驶员年龄 Xage 肇事驾驶员年龄/岁 (37.35) (9.528) (38.15) (9.82) 驾驶员性别 Xsex 男性 0 584 86.4 338 86.4 女性 1 92 13.6 53 13.6 驾驶员是否超车 Xover 否 0 554 82.0 325 83.1 是 1 122 18.0 66 16.9 是否与货车有关 Xtruck 否 0 522 77.2 312 79.8 是 1 154 22.8 79 20.2 是否与摩托车有关 Xmotor 否 0 621 91.9 186 47.6 是 1 55 8.1 205 52.4 事故发生季节 Xseason 春季(3~5月） 1 138 20.4 93 23.8 夏季(6~8月） 2 113 16.7 96 24.6 秋季(9~11月） 3 141 20.9 67 17.1 冬季(12~2月） 4 284 42.0 135 34.5 事故发生日期 Xday 非节假日 0 463 68.5 279 71.4 节假日(周六、周日及法定节假日） 1 213 31.5 112 28.6 事故发生时间 Xtime 白天（07:00—18:00) 0 468 69.2 245 62.7 夜晚（18:00—07:00) 1 208 30.8 146 37.3 天气 Xweather 晴天 0 641 94.8 361 92.3 不利天气 1 35 5.2 30 7.7 路表 Xsurface 路表干燥 0 641 94.8 364 93.1 路表潮湿 1 35 5.2 27 6.9 弯道是否有接人口 Xport 否 0 478 70.7 295 75.4 是 1 198 29.3 96 24.6 弯道转角 Xangle 平曲线转角 (51.48) (24.35) (67.27) (72.59) 弯道长度 Xclength 平曲线长度 (386.85) (119.64) (416.27) (131.64) 弯道坡度 Xslope 弯道的纵坡坡度 (0.56) (1.27) (0.56) (1.31) 弯道坡长 Xlength 弯道的纵坡坡长 (112.91) (267.11) (118.99) (295.39) 注：括号内的数值表示连续变量的均值和标准差。 
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表 2 追尾碰撞模型参数对比
Table 2. Comparison of model parameters for rear-end collisions
模型参数 BL模型 RPBL模型 对数似然值 -161.348 -158.145 AIC 350.3 340.7 McFadden Pseudo R2 0.279 0.281
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表 3 正面碰撞模型参数对比
Table 3. Comparison of model parameters for head-on collisions
模型参数 BL模型 RPBL模型 对数似然值 -142.271 -139.764 AIC 313.5 300.5 McFadden Pseudo R2 0.276 0.279
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表 4 侧面碰撞模型参数对比
Table 4. Comparison of model parameters for side collisions
模型参数 BL模型 RPBL模型 对数似然值 -73.363 -68.116 AIC 170.2 158.7 McFadden Pseudo R2 0.437 0.446
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表 5 追尾碰撞的模型参数估计结果
Table 5. Estimation results of model parameters for rear-end collisions
变量名 BL模型 RPBL模型 平均弹性系数/% 参数估计 z 参数估计 z 驾驶员年龄 -0.050*** -4.28 -0.041*** -6.16 -0.7 驾驶员性别 -0.424 -0.92 是否超车 0.234 0.51 是否与货车有关 0.351 0.95 是否与摩托车有关事故发生季节(春季)# 3.045*** 7.88 2.716***(1.564***) 7.85(3.42) 55.3 事故发生季节(夏季） -0.875** -1.96 11.3 事故发生季节(秋季） -0.977** -2.32 10.3 事故发生季节(冬季） -1.209*** -3.23 -1.495***(2.116***) -14.5 事故发生日期 -0.071 -0.22 事故发生时间 0.818*** 2.67 0.689*** 2.90 10.0 天气 -0.715 -0.94 路表 0.175 0.18 弯道是否有接人口 0.058 0.19 弯道转角 0.015*** 2.74 0.011*** 2.66 0.2 弯道长度 -0.001 -0.89 弯道坡度 0.211 1.35 弯道坡长 0.001 0.05 注：#表示参控变量；* **和***分别表示在0.05和0.001水平显著；括号内数值为该参数的标准误差。
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表 6 正面碰撞的模型参数估计结果
Table 6. Estimation results of model parameters for head-on collisions
变量名 BL模型 RPBL模型 平均弹性系数/% 参数估计 z 参数估计 z 驾驶员年龄 -0.006 -0.51 驾驶员性别 0.101 0.24 是否超车 0.712** -2.14 0.644** 2.42 14.6 是否有货车参与 0.621* 1.72 0.689** 2.43 108.8 是否有摩托车参与 2.859*** 7.54 6.941***(9.901***) 4.65(3.80) 54.4 事故发生季节(春季)# - - - - - 事故发生季节(夏季） -0.055 -0.12 事故发生季节(秋季） -1.284 -2.76 事故发生季节(冬季） -0.595 -1.58 事故发生日期 0.055 0.16 事故发生时间 0.375 1.11 天气 0.872 0.98 路表 0.011 0.01 弯道是否有接人口 -0.176 -0.43 弯道转角 0.009** 2.11 0.006** 1.60 0.2 弯道长度 -0.003** -2.48 -0.004***(0.003***) -4.46(4.98) -0.1 弯道坡度 -0.012 -0.05 弯道坡长 -0.001 -0.87
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表 7 侧面碰撞的模型参数估计结果
Table 7. Estimation results of model parameters for side collision
变量名 BL模型 RPBL模型 平均弹性系数/% 参数估计 z 参数估计 z 驾驶员年龄 -0.045** -2.2 -0.029*** -4.99 -0.5 驾驶员性别 0.087 0.11 是否超车 -0.486 -0.77 是否与货车有关 0.542 0.93 是否与摩托车有关 3.803*** 6.92 5.211***(5.111***) 4.84(3.57) 65.7 事故发生季节(春季)# - - - - - 事故发生季节(夏季） 0.947 1.3 事故发生季节(秋季） 0.017 0.02 事故发生季节(冬季） -0.884 -1.62 事故发生日期 0.731 1.37 事故发生时间 0.206 0.4 天气 -0.883 -0.49 路表 -0.955 -0.46 弯道是否有接人口 -1.007 -1.78 -1.408***(2.146***) -2.32(2.88) -10.5 弯道转角 -0.001 -0.09 弯道长度 0.001 0.16 弯道坡度 -0.505 -1.75 弯道坡长 0.001 0.86
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表 8 典型事故形态致因结果对比
Table 8. Comparison of causative results of typical accident
显著变量 追尾碰撞 正面碰撞 侧面碰撞 驾驶员年龄 ↓ ↓ 是否有货车参与 ↑ 是否与摩托车有关 *↑ *↑ *↑ 事故发生季节_夏 ↑ 事故发生季节_秋 ↑ 事故发生季节_冬 *↓ 事故发生时间 ↑ 弯道转角 ↑ ↑ 弯道长度 *↓ 弯道是否有接人口 *↓ 注：*表示随机参数变量。
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