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考虑距离影响的公交网络敏感度

杜牧青 虞春滨

杜牧青, 虞春滨. 考虑距离影响的公交网络敏感度[J]. 西南交通大学学报, 2021, 56(1): 138-146. doi: 10.3969/j.issn.0258-2724.20190453
引用本文: 杜牧青, 虞春滨. 考虑距离影响的公交网络敏感度[J]. 西南交通大学学报, 2021, 56(1): 138-146. doi: 10.3969/j.issn.0258-2724.20190453
DU Muqing, YU Chunbin. Sensitivity Analysis for Transit Network with Distance Effect[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 138-146. doi: 10.3969/j.issn.0258-2724.20190453
Citation: DU Muqing, YU Chunbin. Sensitivity Analysis for Transit Network with Distance Effect[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 138-146. doi: 10.3969/j.issn.0258-2724.20190453

考虑距离影响的公交网络敏感度

doi: 10.3969/j.issn.0258-2724.20190453
基金项目: 国家自然科学基金(71801079);中央高校基本科研业务费专项基金资助(B200202079)
详细信息
    作者简介:

    杜牧青(1986—),男,副教授,博士,研究方向为交通运输规划与管理,E-mail:dumqhhu@hhu.edu.cn

  • 中图分类号: U491

Sensitivity Analysis for Transit Network with Distance Effect

  • 摘要: 为更加准确地分析公交网络的性能状况,考虑了距离对于出行者感知费用的放大作用,提出了一种广义出行费用函数对公交网络敏感度进行了分析. 首先,分析了距离的影响作用,建立了一个公交网络模型,并通过超路径理论解释了公交共线问题和乘客的选择行为. 其次,在均衡配流的公交网络基础上,根据变分不等式的扰动问题,构建了考虑距离因素的公交网络敏感度分析方法. 最后,以实际算例分析了公交网络在受到不同因素的影响时各个弧段以及网络性能的变化情况,并着重分析了距离因素的影响作用. 研究结果表明:需求从0增加到900人/h,考虑距离影响下的最优出行策略从原来的单一超路径变为多条超路径;在模型关键参数的识别中发现,距离因素是一个较为敏感的参数,需要更加准确地进行标定;在相同的需求水平下,距离参数取值从0变化至0.03时,总旅行费用关于公交服务频率的偏导数值不断减小,为寻找总旅行费用可降低区域提供依据.

     

  • 图 1  感知费用示意

    Figure 1.  Diagram of perceptual cost

    图 2  公交网络节点扩展示意

    Figure 2.  Expansion diagram of transit network nodes

    图 3  线路连接点流量流向关系

    Figure 3.  Flow relationship of line connection point

    图 4  公交线路共线及出行策略

    Figure 4.  Bus common line and travel strategies

    图 5  简单公交网络

    Figure 5.  Simple transit network

    图 6  扩展的公交网络示意

    Figure 6.  Expansion diagram of transit network

    图 7  超路径广义费用情况

    Figure 7.  Generalized cost of hyperpaths

    图 8  距离敏感度的影响

    Figure 8.  Influence of distance sensitivity

    表  1  参数取值

    Table  1.   Parameter values

    参数取值参数取值参数取值
    α1 1.0 β2 0.2 α4 1.1
    β1 1.0 γ1 1.2 d 0.01
    α2 1.0 ρ 2.0 m 1.0
    下载: 导出CSV

    表  2  T对于不同参数偏导数值

    Table  2.   Derivatives of T with respect to different parameters

    ρCbusdα1
    3556.05438−335.252632169.7037325465.71138
    下载: 导出CSV

    表  3  有无距离因素对比分析

    Table  3.   Comparative analysis with and without distance factors

    变量 x不考虑距离 (d = 0)考虑距离 (d = 0.02)
    $\partial x/\partial {f_5} $$\partial x/\partial {f_6} $$\partial x/\partial {f_7} $$\partial x/\partial {f_5} $$\partial x/\partial {f_6} $$\partial x/\partial {f_7} $
    v5 3 345 −2 354 −9 032 2 732 −2 250 −9 046
    v6 −1 005 707 2 713 −821 676 2 717
    v7 −2 340 1 647 6 319 −1 911 1 575 6 329
    T −28 775 −32 210 13 548 −19 855 −38 362 −4 951
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-05
  • 修回日期:  2019-07-18
  • 网络出版日期:  2020-04-09
  • 刊出日期:  2021-02-01

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