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基于日变交通配流的城市道路网络韧性评估

吕彪 高自强 管心怡 刘一骝

吕彪, 高自强, 管心怡, 刘一骝. 基于日变交通配流的城市道路网络韧性评估[J]. 西南交通大学学报, 2020, 55(6): 1181-1190. doi: 10.3969/j.issn.0258-2724.20191214
引用本文: 吕彪, 高自强, 管心怡, 刘一骝. 基于日变交通配流的城市道路网络韧性评估[J]. 西南交通大学学报, 2020, 55(6): 1181-1190. doi: 10.3969/j.issn.0258-2724.20191214
LÜ Biao, GAO Ziqiang, GUAN Xinyi, LIU Yiliu. Resilience Assessment of Urban Road Network Based on Day-to-Day Traffic Assignment[J]. Journal of Southwest Jiaotong University, 2020, 55(6): 1181-1190. doi: 10.3969/j.issn.0258-2724.20191214
Citation: LÜ Biao, GAO Ziqiang, GUAN Xinyi, LIU Yiliu. Resilience Assessment of Urban Road Network Based on Day-to-Day Traffic Assignment[J]. Journal of Southwest Jiaotong University, 2020, 55(6): 1181-1190. doi: 10.3969/j.issn.0258-2724.20191214

基于日变交通配流的城市道路网络韧性评估

doi: 10.3969/j.issn.0258-2724.20191214
基金项目: 教育部人文社会科学研究青年基金(18YJC630115);中央高校基本科研业务费专项资金(2682018CX28)
详细信息
    作者简介:

    吕彪(1980—),男,讲师,博士,研究方向为复杂交通系统建模与优化、智能交通系统理论与方法,E-mail:swjtu_lb@126.com

  • 中图分类号: U491

Resilience Assessment of Urban Road Network Based on Day-to-Day Traffic Assignment

  • 摘要: 为有效评价重大扰动事件下的路网性能,提出以日变交通配流(day-to-day traffic assignment,DTA)为基础的城市道路网络韧性评估模型. 明确考虑重大扰动事件下交通流动态变化特性,构建了综合考虑出行者认知更新、行为惯性等因素影响的DTA模型,设计了启发式求解算法;定义了基于DTA的路网可达性指标,构建了可全面评价扰动事件生命周期内系统性能的韧性指标与评估模型,并在Nguyen & Dupuis网络上进行算例研究. 结果表明:在扰动事件后的前10天,路网韧性波动变化,此后随着交通流分布趋于稳定,路网韧性单调上升,从第10天的0.323上升到第50天的0.794,上升了145.77%;与传统随机用户均衡( stochastic  user  equilibrium,SUE)模型相比,DTA模型获得的路网可达性与韧性指标存在显著差异,SUE模型下路网可达性随时间单调上升,而DTA模型下路网可达性在前15天剧烈波动,随后才单调增加,表明要获得准确的路网韧性指标,必须首先准确假定出行决策行为和相应配流模型;出行者行为惯性、路段通行能力退化程度与恢复速率以及路网拥挤程度等因素均对交通流量分布产生显著影响,进而影响路网可达性最终导致路网韧性指标发生显著变化,表明实际应用中应在充分调查的基础上合理标定相关参数.

     

  • 图 1  扰动事件下系统状态变化

    Figure 1.  System state transitions subject to disruptive event

    图 2  系统韧性的图解表示

    Figure 2.  Graphical representation of system resilience

    图 3  Nguyen & Dupuis网络

    Figure 3.  Nguyen & Dupuis network

    图 4  随时间变化的路径流量、期望理解出行时间及实际出行时间

    Figure 4.  Time-varying route flow,mean perceived travel time,and actual travel time

    图 5  两种韧性指标的比较

    Figure 5.  Comparison between two different resilience metrics

    图 6  使用不同配流模型获得的路网可达性与韧性

    Figure 6.  Road network accessibility and resilience using different traffic assignment models

    图 7  参数对路网韧性的影响

    Figure 7.  Effects of different values on network resilience

    表  1  路段路径关联关系

    Table  1.   Link-route incidence relationship

    OD对路径编号路段序列
    (1,2)12→18→11
    22→17→7→9→11
    32→17→7→10→15
    42→17→8→14→15
    51→5→7→9→11
    61→5→7→10→15
    71→5→8→14→15
    81→6→12→14→15
    (1,3)92→17→7→10→16
    102→17→8→14→16
    111→5→7→10→16
    121→5→8→14→16
    131→6→13→19
    141→6→12→14→16
    (4,2)153→5→7→9→11
    163→5→7→10→15
    173→5→8→14→15
    183→6→12→14→15
    194→12→14→15
    (4,3)203→5→7→10→16
    213→5→8→14→16
    223→6→12→14→16
    234→12→14→16
    244→13→19
    253→6→13→19
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-29
  • 修回日期:  2020-03-07
  • 网络出版日期:  2020-07-21
  • 刊出日期:  2020-12-15

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