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考虑风险爱好驾驶人的相依Weibull随机交通分配模型

俞礼军 谈进舟

俞礼军, 谈进舟. 考虑风险爱好驾驶人的相依Weibull随机交通分配模型[J]. 交通运输工程学报, 2017, 17(6): 76-85.
引用本文: 俞礼军, 谈进舟. 考虑风险爱好驾驶人的相依Weibull随机交通分配模型[J]. 交通运输工程学报, 2017, 17(6): 76-85.
YU Li-jun, TAN Jin-zhou. Weibull dependence stochastic traffic assignment model considering risk prone drivers[J]. Journal of Traffic and Transportation Engineering, 2017, 17(6): 76-85.
Citation: YU Li-jun, TAN Jin-zhou. Weibull dependence stochastic traffic assignment model considering risk prone drivers[J]. Journal of Traffic and Transportation Engineering, 2017, 17(6): 76-85.

考虑风险爱好驾驶人的相依Weibull随机交通分配模型

基金项目: 

国家自然科学基金项目 61473122

详细信息
    作者简介:

    俞礼军(1972-), 男, 新疆阿拉尔人, 华南理工大学副教授, 工学博士, 从事交通运输规划研究

  • 中图分类号: U491

Weibull dependence stochastic traffic assignment model considering risk prone drivers

More Information
  • 摘要: 建立了考虑风险爱好驾驶人的相依Weibull随机交通分配(Weibull-DSA) 模型, 分析了感知等价路径负效用的Weibull边缘生存函数, 假设驾驶人总是选择感知等价路径负效用最小的路径到达目的地, 采用Copula方法构建了感知等价路径负效用的联合生存函数, 预测了路径选择概率; 设计了模型的迭代求解算法, 对模型进行了理论分析和数值验证; 研究了广州市交通调查获得的风险系数, 基于风险爱好和风险中立驾驶人, 比较了采用Weibull-DSA模型与经典的Logit-SUE和Weibit-SUE模型计算的路径选择概率、路段交通量、饱和度与系统总出行时间。计算结果表明: 随着风险系数的降低, 3种分配模型的交通系统总出行时间变大; 在风险中立情况下, 应用Weibull-DSA模型、Logit-SUE模型和Weibit-SUE模型计算得到每OD对的所有连接路径选择概率的最大差值, 分别为0.17、0.33、0.34, 在风险爱好情况下, 由3种模型得到的最大差值分别为0.20、0.36、0.41, 因此, 采用Weibull-DSA模型计算得到的不同路径选择概率的最大差值明显小于经典模型计算得到的最大差值; 相对于风险中立情况, 风险系数使得每OD对的所有连接路径选择概率的最大差值变大; 无论是风险爱好还是风险中立驾驶人, 采用Logit-SUE和Weibit-SUE模型计算得到的路段饱和度均小于0.9, 采用Weibull-DSA模型计算得到路段饱和度大于0.9;与经典模型计算结果不同, 采用Weibull-DSA模型得到的不同路径选择概率的最大差值相差较小, 一些路径获得更多交通量, 使得路径中通行能力最小的路段的饱和度大于0.9, 这一特征给出了城市路网中部分瓶颈路段拥堵现象一个新的解释。

     

  • 图  1  算例路网

    Figure  1.  Example network

    图  2  连接OD对1-5的6条路径的路径选择概率

    Figure  2.  Route choice probabilities of six alternative routes connecting OD pair 1-5

    图  3  连接OD对5-1的6条路径的路径选择概率

    Figure  3.  Route choice probabilities of six alternative routes connecting OD pair 5-1

    图  4  连接OD对3-7的6条路径的路径选择概率

    Figure  4.  Route choice probabilities of six alternative routes connecting OD pair 3-7

    图  5  连接OD对7-3的6条路径的路径选择概率

    Figure  5.  Route choice probabilities of six alternative routes connecting OD pair 7-3

    图  6  风险中立情况下Logit-SUE模型得到的路段饱和度

    Figure  6.  Saturation degrees of road sections resulting from Logit-SUE model under risk neutral condition

    图  7  风险中立情况下Weibit-SUE模型得到的路段饱和度

    Figure  7.  Saturation degrees of road sections resulting from Weibit-SUE model under risk neutral condition

    图  8  风险中立情况下Weibull-SUE模型得到的路段饱和度

    Figure  8.  Saturation degrees of road sections resulting from Weibull-SUE model under risk neutral condition

    图  9  风险爱好情况下Logit-SUE模型得到的路段饱和度

    Figure  9.  Saturation degrees of road sections resulting from Logit-SUE model under risk prone condition

    图  10  风险爱好情况下Weibit-SUE模型得到的路段饱和度

    Figure  10.  Saturation degrees of road sections resulting from Weibit-SUE model under risk prone condition

    图  11  风险爱好情况下Weibull-SUE模型得到的路段饱和度

    Figure  11.  Saturation degrees of road sections resulting from Weibull-SUE model under risk prone condition

    图  12  系统总出行时间

    Figure  12.  Total system travel times

    表  1  等价路段负效用函数的参数

    Table  1.   Parameters of equivalent route disutility function

    下载: 导出CSV

    表  2  OD需求量与OD对之间的一组出行路径

    Table  2.   OD demands and sets of routes between each OD pair

    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-07-19
  • 刊出日期:  2017-12-25

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