留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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
  • [1] 裴玉龙, 马骥. 道路交通事故道路条件成因分析及预防对策研究[J]. 中国公路学报, 2003, 16(4): 77-82. doi: 10.3321/j.issn:1001-7372.2003.04.017

    PEI Y L, MA J. Research on countermeasures for road condition causes of traffic accidents[J]. China Journal of Highway and Transport, 2003, 16(4): 77-82. (in Chinese) doi: 10.3321/j.issn:1001-7372.2003.04.017
    [2] 孟祥海, 张晓明, 郑来. 基于线形与交通状态的山区高速公路追尾事故预测[J]. 中国公路学报, 2012, 25(4): 113-118. doi: 10.3969/j.issn.1001-7372.2012.04.019

    MENG X H, ZHANG X M, ZHENG L. Prediction of rear-end collision on mountainous expressway based on geometric alignment and traffic conditions[J]. China Journal of Highway and Transport, 2012, 25(4): 113-118. (in Chinese) doi: 10.3969/j.issn.1001-7372.2012.04.019
    [3] 马壮林, 邵春福, 胡大伟, 等. 高速公路交通事故起数时空分析模型[J]. 交通运输工程学报, 2012, 12(2): 93-99. doi: 10.3969/j.issn.1671-1637.2012.02.015

    MA Z L, SHAO C F, HU D W, et al. Temporal-spatial analysis of traffic accident frequency on expressway[J]. Journal of Traffic and Transportation Engineering, 2012, 12(2): 93-99. (in Chinese) doi: 10.3969/j.issn.1671-1637.2012.02.015
    [4] MA Z, ZHANG H, CHIEN S I, et al. Predicting expressway crash frequency using a random effect negative binomial model: A case study in china[J]. Accident Analysis & Prevention, 2017(98): 214-222. http://www.sciencedirect.com/science?_ob=ShoppingCartURL&_method=add&_eid=1-s2.0-S0001457516303736&originContentFamily=serial&_origin=article&_ts=1496245739&md5=49c18f0d132b466ab08b1d11dcf02a1d
    [5] ANASTASOPOULOS P C, MANNERING F L. A note on modeling vehicle accident frequencies with random-parameters count models[J]. Accident Analysis & Prevention, 2009, 41(1): 153-159. http://www.onacademic.com/detail/journal_1000034047526310_2ca8.html
    [6] RUSLI R, HAQUE M M, KING M, et al. Single-vehicle crashes along rural mountainous highways in Malaysia: an application of random parameters negative binomial model[J]. Accident Analysis & Prevention, 2017(102): 153-164. http://www.onacademic.com/detail/journal_1000039848441410_0a92.html
    [7] 付锐, 郭应时, 袁伟, 等. 连续下坡道路事故率与纵面参数关系研究[J]. 中国公路学报, 2009, 22(3): 101-106. doi: 10.3321/j.issn:1001-7372.2009.03.018

    FU R, GUO Y S, YUAN W, et al. Research on relation of traffic accident rate and longitudinal parameters in continuous downgrade road[J]. China Journal of Highway and Transport, 2009, 22(3): 101-106. (in Chinese) doi: 10.3321/j.issn:1001-7372.2009.03.018
    [8] MONTELLA A, IMBRIANI L L. Safety performance functions incorporating design consistency variables[J]. Accident Analysis & Prevention, 2015(74), 133-144. http://www.onacademic.com/detail/journal_1000036798236510_58e3.html
    [9] 李明, 王永岗, 张巍, 等. 山区高速公路交通死亡事故显著影响因素鉴别[J]. 中国公路学报, 2015, 25(5): 126-135. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201505025.htm

    LI M, WANG Y G, ZHANG W, et al. Identifying significant factors influencing occurrence of fatal crash on mountainous freeway[J]. China Journal of Highway and Transport, 2015, 25(5): 126-135. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201505025.htm
    [10] 孟祥海, 侯芹忠, 史永义, 等. IHSDM高速公路事故预测模型[J]. 交通运输工程学报, 2016, 16(1): 123-132. doi: 10.3969/j.issn.1671-1637.2016.01.015

    MENG X H, HOU Q Z, SHI Y Y, et al. IHSDM freeway accident prediction models[J]. Journal of Traffic and Transportation Engineering, 2016, 16(1): 123-132. (in Chinese) doi: 10.3969/j.issn.1671-1637.2016.01.015
    [11] 陈昭明, 徐文远, 曲悠扬, 等. 基于混合Logit模型的高速公路交通事故严重程度分析[J]. 交通信息与安全, 2019, 37(3): 42-50. doi: 10.3963/j.issn.1674-4861.2019.03.006

    CHEN Z M, XU W Y, QU Y Y, et al. Analysis of freeway traffic crash severity based on mixed logit model[J]. Journal of Transport Information and Safety, 2019, 37(3): 42-50. (in Chinese) doi: 10.3963/j.issn.1674-4861.2019.03.006
    [12] ABDEL-ATY M, RADWAN A. Modeling traffic accident oc-currence and involvement[J]. Accident Analysis & Prevention, 2000, 32(5): 633-642.
    [13] NAIK B, TUNG L W, ZHAO S, et al. Weather impacts on single-vehicle truck crash injury severity[J]. Journal of Safety Research, 2016(58): 57-65. http://daneshyari.com/article/preview/587262.pdf
    [14] BUDDHAVARAPU P, BANERJEE A, PROZZI J A. Influence of pavement condition on horizontal curve safety[J]. Accident Analysis & Prevention, 2013(52): 9-18. http://www.sciencedirect.com/science/article/pii/S0001457512004320
    [15] HUO X, LENG J, HOU Q. A correlated random parameters model with heterogeneity in means to account for unobserved heterogeneity in crash frequency analysis[J]. Transportation Research Record, 2020(2674): 312-322.
    [16] KIM J K, ULFARSSON G F, SHANKAR V N, et al. A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model[J]. Accident Analysis & Prevention, 2010, 42(6): 1751-1758. http://www.sciencedirect.com/science?_ob=ShoppingCartURL&_method=add&_eid=1-s2.0-S0001457510001326&originContentFamily=serial&_origin=article&_ts=1475414046&md5=f52196c46c1700f00e62fe9b5e062a00
    [17] 于蕾, 张金喜. 浅谈路面性能间断性对行车安全的影响[J]. 中国科技信息, 2013(13): 134-136. doi: 10.3969/j.issn.1001-8972.2013.13.078

    YU L, ZHANG J X. Summarization to influence of roadway surface discontinuities on safety[J]. China Science and Technology Information, 2013(13): 134-136. (in Chinese) doi: 10.3969/j.issn.1001-8972.2013.13.078
    [18] INTINI P, BERLOCO N, RANIERI V, et al. Geometric and operational features of horizontal curves with specific regard to skidding proneness[J]. Infrastructures, 2020, 5(3): 1-25. http://www.researchgate.net/publication/338245289_Geometric_and_Operational_Features_of_Horizontal_Curves_with_Specific_Regard_to_Skidding_Proneness
    [19] CAFISO S, MONTELLA A, AGOSTINO C, et al. Crash modification functions for pavement surface condition and geometric design indicators[J]. Accident Analysis & Prevention, 2021(149): 105887.
    [20] HUSSEIN N, HASSAN R, FAHEY T. Effect of pavement condition and geometrics at signalised intersections on casualty crashes[J]. Journal of Safety Research, 2021(76): 276-288. http://www.sciencedirect.com/science/article/pii/S0022437520301717
    [21] 胡思涛, 项乔君. 高速公路路面状态安全评价方法研究[J]. 交通运输系统工程与信息, 2013, 13(2): 130-135. doi: 10.3969/j.issn.1009-6744.2013.02.019

    HU S T, XIANG Q J. Safety evaluation method of freeway pavement condition[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(2): 130-135. doi: 10.3969/j.issn.1009-6744.2013.02.019
    [22] HOU Q, HUO X, LENG J. A correlated random parameters tobit model to analyze the safety effects and temporal instability of factors affecting crash rates[J]. Accident Analysis & Prevention, 2020(134): 105326. http://www.sciencedirect.com/science/article/pii/S0001457518308145
    [23] CHEN E, TARKO A P. Modeling safety of highway work zones with random parameters and random effects models[J]. Analytic Methods in Accident Research, 2014(1): 86-95. http://www.onacademic.com/detail/journal_1000036200278010_baa8.html
    [24] BHAT C R. A new generalized heterogeneous data model (GHDM) to jointly model mixed types of dependent variables[J]. Transportation Research Part B: Methodological, 2015(79): 50-77. http://trid.trb.org/view/1339488
    [25] MANNERING F L, SHANKAR V, BHAT C R. Unobserved heterogeneity and the statistical analysis of highway accident data[J]. Analytic Methods in Accident Research, 2016(11): 1-16. http://cee.eng.usf.edu/faculty/flm/CGN6933/Heterogeneity.pdf
    [26] HAUER E. The art of regression modeling in road safety[M]. New York: Springer, 2015.
  • 加载中
图(1) / 表(3)
计量
  • 文章访问数:  1365
  • HTML全文浏览量:  570
  • PDF下载量:  116
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-08-26
  • 网络出版日期:  2022-03-31

目录

    /

    返回文章
    返回