An Analysis of Severity of Traffic Accidents on Urban Roadways Based on Binary Logistic Models
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摘要: 为从多维度精准剖析影响城市道路交通事故严重程度的因素，选取了我国某城市2018—2020年交通事故数据库中的4 587条数据作为研究对象，基于二元Logistic模型，从人、车、路、环境这4个方面，分别针对财产损失事故、伤人事故、死亡事故建立了模型。深入分析了道路物理隔离位置、路侧防护设施类型等因素对事故严重程度的影响，并利用Hosmer-Lemeshow检验和一致性检验对模型有效性进行验证。结果表明：①道路物理隔离的空间位置对事故严重程度有显著影响，仅布设中心隔离设施发生死亡事故的概率是同时布设中央和机非隔离的2.304倍。在有中心隔离设施的高等级道路中，增设机非隔离设施能有效降低事故发生的概率。②路侧防护设施类型为行道树、绿化带时，发生死亡事故的概率分别是金属护栏的1.982倍、1.648倍。与金属护栏相比，行道树更容易引发严重事故。③夜间无路灯照明发生死亡事故的概率是夜间有路灯照明的1.808倍，夜间无路灯照明是导致死亡事故的重要因素之一。④受过高等教育的驾驶人发生财产损失事故和伤人事故的概率较高，受过中等教育的驾驶员发生死亡事故的概率较高；受过中等教育驾驶员发生死亡事故的概率是高等教育驾驶员的2.049倍。研究深入分析了影响城市道路交通事故的显著因素及其对事故的影响，为事故严重程度的精细化分析提供了理论支持，为交通规划与管理部门提供了决策依据。
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关键词:
- 交通安全 /
- 事故影响因素 /
- 事故严重程度 /
- 二元Logistic回归分析
Abstract: In order to accurately identify the factors affecting the severity of traffic accidents on urban roadways, 4 587 records from the traffic accident database of a city in China from Year 2018 to 2020 are used. A series of binary logistic models are developed for property damage accidents, personal-injury accidents, and fatal accidents based on the data regarding the following four aspects, including participants, vehicles, roads, and environment. The impacts of location of dividing strips and the types of roadside protection facilities on the severity of accidents are analyzed. Further, the Hosmer-Lemeshow tests and the consistency tests are used to check the soundness of the models. The results from the binary Logistic models show that: ① locations of dividing strips have a significant effect on the severity of the accidents. The probability of fatal accidents of placing central dividing strips only is 2.304 times higher than placing both central and motor/non-motor dividing strips. On high-grade roads with central dividing strips, adding motorized/non-motorized dividing strips can effectively reduce the probability of accidents. ② The probability of fatal accidents for the roadside protection facilities being street trees and green belts is 1.982 times and 1.648 times higher than the roadside protection facilities being metal guardrails, respectively. Street trees as the type of roadside protection facilities are likely to lead to more serious accidents than metal guardrails. ③The probability of fatal accidents at night "with no streetlight" is 1.808 times higher than that "with streetlights" at night. No streetlight at night is a significant risk factor leading to the fatal accidents. ④ Drivers with a high-level of education are more likely to have property-damage accidents and personal-injury accidents. Fatal accidents are more likely to occur among drivers with a medium-level of education, and their probability of resulting in the fatal accidents is 2.049 times higher than drivers with a high-level of education. In conclusion, this study presents an analysis of the factors and their impacts on traffic accidents at urban roadways. Furthermore, it can serve as a theoretical support for the refined analysis of accident severity on urban roadways, as well as a reference for traffic safety planning and management authorities. -
图 2 不同严重程度事故发生的验证概率
Figure 2. Probability of the occurrence of accidents of different severity
表 1 因变量描述性统计与赋值
Table 1. Descriptive statistics and assignment of dependent variables
变量类型 变量名称 变量频数 变量赋值 事故严重程度 财产损失事故 666 发生财产损失事故：Y=1;不发生财产损失事故：Y=0 伤人事故 3 375 发生伤人事故：Y=1;不发生伤人事故：Y=0 死亡事故 545 发生死亡事故：Y=1;不发生死亡事故：Y=0 
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表 2 自变量描述性统计与赋值
Table 2. Descriptive statistics and assignment of independent variables
变量类型 变量名称与类别 变量统计 赋值 道路条件 道路物理隔离 无隔离 2 499 1 机非隔离 297 2 中心隔离加机非隔离 677 3 中心隔离 1 113 4 路侧防护设施类型 无防护 1 916 1 绿化带 1 574 2 行道树 839 3 金属护栏 257 4 路口路段类型 路段 2 676 1 交叉口 1 910 2 交通信号方式 无控制 1 616 1 有控制 2 970 2 环境 天气 晴 3 830 1 阴 469 2 雨 251 3 雪 21 4 雾 15 5 照明条件 白天 2 919 1 夜间有路灯照明 1 093 2 夜间无路灯照明 574 3 能见度/m <50 300 1 50~100 726 2 101~200 1 009 3 >200 2 549 4 驾驶人 性别 女 4 034 1 男 552 2 受教育程度 高等教育 1 696 1 中等教育 2 890 2 年龄/岁 ≤25 566 1 >25~35 1 581 2 >35~45 1 326 3 >45~55 960 4 >55 154 5 驾龄/年 ≤2 425 1 >2~5 887 2 >5~10 1 433 3 >10~15 880 4 >15 962 5 车辆 行驶状态 直行 3 731 1 非直行 856 2
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表 3 共线性检验结果
Table 3. Collinearity test result
变量名称 容忍度 VIF 道路物理隔离 0.87 1.15 路侧防护设施类型 0.89 1.12 路口路段类型 0.97 1.04 交通信号方式 0.95 1.05 天气 0.99 1.02 照明条件 0.8 1.25 能见度 0.81 1.24 性别 0.96 1.05 驾龄 0.7 1.44 年龄 0.70 1.45 受教育程度 0.94 1.07 行驶状态 0.96 1.04
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表 4 模型标定结果
Table 4. Model calibration results
财产损失事故 伤人事故 死亡事故 影响因素 B 影响因素 B 影响因素 B 路口路段类型(1=路段) 0.252 道路物理隔离 道路物理隔离 交通信号方式(1=无控制) 0.329 道路物理隔离(1=无隔离) 0.235 道路物理隔离(1=无隔离) -0.406 照明条件 道路物理隔离(2=机非隔离) 0.193 道路物理隔离(2=机非隔离) -0.566 照明条件(1=白天) -0.308 道路物理隔离(3=中心隔离加机非隔离) 0.288 道路物理隔离(3=中心隔离加机非隔离) -0.835 照明条件(2=夜间有路灯照明) 1.078 路口路段类型(1=路段) -0.288 路侧防护设施类型 年龄/岁 照明条件 路侧防护设施类型(1=无防护) 0.128 年龄(1= <25) 0.509 照明条件(1=白天) 0.623 路侧防护设施类型(2=绿化带) 0.499 年龄(2= >25~35) 0.798 照明条件(2=夜间有路灯照明) -0.394 路侧防护设施类型(3=行道树) 0.684 年龄(3= >35~45) 0.438 受教育程度(1=高等教育) 0.178 路口路段类型(1=路段) 0.245 年龄(4= >45~55) 0.439 行驶状态(1=直行) -0.446 交通信号方式(1=无控制) -0.356 受教育程度(1=高等教育) 0.269 常量 1.065 照明条件 行驶状态(1=直行) 0.451 照明条件(1=白天) -0.716 常量 -3.306 照明条件(2=夜间有路灯照明) -0.592 受教育程度(1=高等教育) -0.718 常量 -1.246
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表 5 财产损失事故与影响因素的二元Logistic回归分析结果
Table 5. Results of binary Logistic regression analysis of property loss accidents and influencing factors
影响因素 B 标准误差 瓦尔德 自由度 显著性 exp(B) 路口路段类型1=路段 0.252 0.097 6.727 1 0.009 1.286 交通信号方式1=无控制 0.329 0.098 11.277 1 0.001 1.39 193.205 2 <0.001 照明条件 1=白天 -0.308 0.151 4.182 1 0.041 0.735 2=夜间有路灯照明 1.078 0.154 48.758 1 <0.001 2.939 16.749 4 0.002 1=≤25 0.509 0.345 2.171 1 0.141 1.663 年龄/岁 2= >25~35 0.798 0.328 5.908 1 0.015 2.222 3= >35~45 0.438 0.333 1.729 1 0.189 1.55 4= >45~55 0.439 0.339 1.68 1 0.195 1.551 受教育程度 1=高等教育 0.269 0.096 7.808 1 0.005 1.308 行驶状态 1=直行 0.451 0.137 10.864 1 0.001 1.569 常量 -3.306 0.375 77.583 1 <0.001 0.037
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表 6 伤人事故与影响因素的二元Logistic回归分析结果
Table 6. Results of binary Logistic regression analysis of injury accidents and influencing factors
影响因素 B 标准误差 瓦尔德 自由度 显著性 exp (B) 8.832 3 0.032 1=无隔离 0.235 0.087 7.285 1 0.007 1.265 道路物理隔离 2=机非隔离 0.193 0.165 1.368 1 0.242 1.213 3=中心隔离加机非隔离 0.288 0.122 5.593 1 0.018 1.334 路口路段类型(1=路段) -0.288 0.076 14.395 1 <0.001 0.75 155.617 2 <0.001 照明条件 1=白天 0.623 0.107 33.755 1 <0.001 1.865 2=夜间有路灯照明 -0.394 0.119 10.973 1 0.001 0.674 受教育程度 1=高等教育 0.178 0.078 5.229 1 0.022 1.195 行驶状态 1=直行 -0.446 0.104 18.481 1 <0.001 0.64 常量 1.065 0.152 49.227 1 <0.001 2.901
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表 7 死亡事故与影响因素的二元Logistic回归分析结果
Table 7. Results of binary Logistic regression analysis of fatal accident and influencing factors
影响因素 B 标准误差 瓦尔德 自由度 显著性 exp(B) 26.983 3 <0.001 1=无隔离 -0.406 0.121 11.289 1 0.001 0.667 道路物理隔离 2=机非隔离 -0.566 0.231 5.987 1 0.014 0.568 3=中心隔离加机非隔离 -0.835 0.183 20.867 1 <0.001 0.434 22.393 3 <0.001 路侧防护设施类型 1=无防护 0.128 0.259 0.243 1 0.622 1.136 2=绿化带 0.499 0.249 4.031 1 0.045 1.648 3=行道树 0.684 0.26 6.92 1 0.009 1.982 路口路段类型 1=路段 0.245 0.102 5.777 1 0.016 1.278 交通信号方式 1=无控制 -0.356 0.108 10.78 1 0.001 0.7 30.707 2 <0.001 照明条件 1=白天 -0.716 0.129 30.621 1 <0.001 0.489 2=夜间有路灯照明 -0.592 0.157 14.229 1 <0.001 0.553 受教育程度 1=高等教育 -0.718 0.116 38.392 1 <0.001 0.488 常量 -1.246 0.265 22.159 1 <0.001 0.288
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表 9 肯德尔(W)检验
Table 9. Kendall(W)test
事故类型 肯德尔(W) 渐进显著性 财产损失事故 0.797 0.144 伤人事故 0.900 0.126 死亡事故 0.910 0.122
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