山区高速公路隧道路段与开放路段的事故影响因素分析

张璇; 唐进君; 黄合来; 常方蓉; 王杰; 袁双林

doi:10.3963/j.jssn.1674-4861.2022.03.002

山区高速公路隧道路段与开放路段的事故影响因素分析

doi: 10.3963/j.jssn.1674-4861.2022.03.002

张璇^1,,
唐进君¹,
黄合来¹,
常方蓉^2, ,,
王杰³,
袁双林⁴

1.
中南大学交通运输工程学院长沙 410075
2.
中南大学资源与安全学院长沙 410083
3.
长沙理工大学交通运输工程学院长沙 410014
4.
衡阳市高速交通警察支队湖南衡阳 421001

基金项目:

国家自然科学基金项目 71971222

详细信息

作者简介:
张璇（1994—），博士研究生. 研究方向：交通安全. E-mail: zhangxuandef@csu.edu.cn

通讯作者:
常方蓉（1991—），博士，讲师. 研究方向：交通安全. E-mail: fangrongchang@csu.edu.cn

中图分类号: U491.3
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出版历程
- 收稿日期: 2021-11-26
- 网络出版日期: 2022-07-25

An Analysis of Influential Factors of Crashes at Tunnels and Open Sections of Mountainous Freeways

1.
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
2.
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
3.
School of Traffic and Transportation Changsha University of Science and Technology, Changsha 410014, China
4.
Hengyang Highway Traffic Police Detachment, Hengyang 421001, Hunan, China

摘要

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。
- 交通安全 /
- 事故严重程度 /
- 高速公路隧道 /
- 影响因素 /
- 均值异质性 /
- 随机参数Logit模型
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.
- road safety /
- crash severity /
- freeway tunnel /
- influential factors /
- heterogeneity /
- random parameter Logit model

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图 1 隧道路段分类示意图

Figure 1. Diagram of tunnel section

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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
变量	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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