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基于负二项分布的高速公路交通事故影响因素分析

陈昭明 徐文远

陈昭明, 徐文远. 基于负二项分布的高速公路交通事故影响因素分析[J]. 交通信息与安全, 2022, 40(1): 28-35. doi: 10.3963/j.jssn.1674-4861.2022.01.004
引用本文: 陈昭明, 徐文远. 基于负二项分布的高速公路交通事故影响因素分析[J]. 交通信息与安全, 2022, 40(1): 28-35. doi: 10.3963/j.jssn.1674-4861.2022.01.004
CHEN Zhaoming, XU Wenyuan. An Analysis of Factors Influencing Freeway Crashes with a Negative Binomial Model[J]. Journal of Transport Information and Safety, 2022, 40(1): 28-35. doi: 10.3963/j.jssn.1674-4861.2022.01.004
Citation: CHEN Zhaoming, XU Wenyuan. An Analysis of Factors Influencing Freeway Crashes with a Negative Binomial Model[J]. Journal of Transport Information and Safety, 2022, 40(1): 28-35. doi: 10.3963/j.jssn.1674-4861.2022.01.004

基于负二项分布的高速公路交通事故影响因素分析

doi: 10.3963/j.jssn.1674-4861.2022.01.004
基金项目: 

国家重点研发计划项目 2016YFC0701605-02

黑龙江省交通运输厅重点科技项目 2017hljjt017

详细信息
    作者简介:

    陈昭明(1990-), 博士研究生. 研究方向: 交通安全与交通环境. E-mail: 540245848@qq.com

    通讯作者:

    徐文远(1969-), 博士, 教授. 研究方向: 道路工程与交通环境. E-mail: xuwenyuan@nefu.edu.cn

  • 中图分类号: U491.31

An Analysis of Factors Influencing Freeway Crashes with a Negative Binomial Model

  • 摘要: 为分析高速公路交通事故的影响因素,构建基于负二项分布的事故分析模型,探究事故数与交通特性、公路线形及路面性能间关系。鉴于传统固定参数模型难以刻画各因素对事故风险影响的异质性,引入了随机参数建模方法。结果表明:相比于固定参数负二项模型,构建的随机参数负二项模型有更好的拟合优度,且能更合理地反映各因素对事故的作用效果;将随机参数分布的均值设置为其他变量的函数形式,可进一步挖掘各因素对事故风险的交互影响;交通量、路段长度、货车比例、平曲线曲率、纵坡坡度及车辙深度均与事故数正相关,且其每增加1%,事故数分别增加0.299%,1.029%,0.093%,0.079%,0.068%和0.054%;结构强度系数与事故数负相关,其每增加1%,事故数降低0.064%;增加路缘带宽度有益于交通安全;单向3车道或4车道路段的事故数多于同等条件下的2车道路段;弯坡组合路段的事故风险明显高于单纯的平曲线路段;货车比例高的下坡路段事故风险尤其高。

     

  • 图  1  累计残差与AADT的关系

    Figure  1.  Cumulative Residuals versus AADT

    表  1  建模变量的统计特性

    Table  1.   Statistics of considered variables for modeling

    变量名称 连续变量 离散变量
    均值 标准差 最小值 最大值 样本量 比例/%
    事故数/次 0.65 1.32 0 21
    路段长度/km 0.55 0.32 0.16 5.37
    AADT/(103 veh/d) 5.66 4.15 2.77 10.23
    货车比例 0.38 0.18 0.16 0.51
    车道数/条
      2* 11 108 56.3
      3 4 558 23.1
      4 4 064 20.6
    路缘带宽度/m
      0.5* 1 144 5.8
      0.75 18 586 94.2
    平曲线曲率(/km) 0.21 0.30 0 2
    纵坡坡度/% 1.00 0.92 0.03 4
    纵坡方向
      上坡* 9 687 49.1
      下坡 10 043 50.9
    路面破损率/% 0.07 0.18 0 4.57
    车辙深度/mm 6.93 2.52 0.13 24
    结构强度系数 2.95 2.15 0.32 6.76
    注:“*”表示该变量为基准变量。
    下载: 导出CSV

    表  2  模型标定结果(剔除不显著变量)

    Table  2.   Estimation results for models (excluded non-significant variables)

    变量名称 固定参数负二项模型 随机参数负二项模型
    参数估计值 标准误 z 参数估计值 标准误 z
    常数项 0.618 0.190 3.247 0.683 0.162 4.215
      参数分布标准差 0.578 0.007 77.282
    AADT的对数# 0.309 0.020 15.694 0.299 0.016 19.124
    路段长度的对数# 0.992 0.021 47.513 1.029 0.019 58.170
    货车比例 0.210 0.077 2.712 0.184 0.080 2.311
    车道数_3 0.087 0.033 2.657 0.069 0.030 2.285
    车道数_4 0.284 0.031 9.077 0.245 0.026 9.396
    路缘带宽度_0.75 m -0.299 0.040 -7.411 -0.270 0.044 -6.107
      参数分布标准差 0.057 0.009 4.094
    平曲线曲率 0.363 0.029 12.355 0.192 0.035 5.534
      均值影响因素:纵坡坡度 0.031 0.012 2.632
      参数分布标准差 0.210 0.022 9.743
    纵坡坡度 0.078 0.011 7.335 0.068 0.010 6.589
    纵坡方向_下坡 0.066 0.020 3.356 0.046 0.017 2.770
      均值影响因素:货车比例 0.102 0.014 2.286
      参数分布标准差 0.231 0.011 20.960
    路面破损率 -0.184 0.085 -2.163
    车辙深度 0.021 0.004 5.897 0.019 0.004 5.154
      参数分布标准差 0.010 0.001 9.213
    结构强度系数 -0.016 0.005 -3.165 -0.020 0.005 -3.815
      参数分布标准差 0.044 0.002 21.010
    过离散参数α 0.617 0.018 35.156 5.148 0.346 14.861
    样本数量 19 730 19 730
    参数数量 13 22
    对数似然值 -26 698 -26 588
    AIC 53 422 53 220
    ρ2 0.147 0.151
    注:“#”为AADT与路段长度为模型中的暴露变量。
    下载: 导出CSV

    表  3  事故次数对各显著变量的敏感性

    Table  3.   Sensitivities of crash for significant variables

    变量名称 弹性系数Ek 边际效应系数Dl 95%置信区间
    AADT 0.299 (0.303,0.371)
    路段长度 1.029 (0.877,0.944)
    货车比例 0.093 (0.038,0.148)
    车道数_3 0.041 (0.013,0.069)
    车道数_4 0.142 (0.113, 0.170)
    路缘带宽度_0.75 m -0.159 (-0.201, -0.117)
    平曲线曲率 0.079 (0.032,0.126)
    纵坡坡度 0.068 (0.033,0.104)
    纵坡方向_下坡 0.026 (0.009,0.042)
    路面破损率 -0.011 (-0.016,-0.005)
    车辙深度 0.054 (0.043,0.065)
    结构强度系数 -0.064 (-0.117, -0.01)
    下载: 导出CSV
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  • 收稿日期:  2021-08-26
  • 网络出版日期:  2022-03-31

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