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拥挤传播对汽车共享系统运行影响的仿真分析

胡路 梁志梅 蒋阳升

胡路, 梁志梅, 蒋阳升. 拥挤传播对汽车共享系统运行影响的仿真分析[J]. 西南交通大学学报, 2023, 58(3): 499-510. doi: 10.3969/j.issn.0258-2724.20220231
引用本文: 胡路, 梁志梅, 蒋阳升. 拥挤传播对汽车共享系统运行影响的仿真分析[J]. 西南交通大学学报, 2023, 58(3): 499-510. doi: 10.3969/j.issn.0258-2724.20220231
HU Lu, LIANG Zhimei, JIANG Yangsheng. Simulation Analysis on Influence of Congestion Propagation on Operation of Carsharing Systems[J]. Journal of Southwest Jiaotong University, 2023, 58(3): 499-510. doi: 10.3969/j.issn.0258-2724.20220231
Citation: HU Lu, LIANG Zhimei, JIANG Yangsheng. Simulation Analysis on Influence of Congestion Propagation on Operation of Carsharing Systems[J]. Journal of Southwest Jiaotong University, 2023, 58(3): 499-510. doi: 10.3969/j.issn.0258-2724.20220231

拥挤传播对汽车共享系统运行影响的仿真分析

doi: 10.3969/j.issn.0258-2724.20220231
基金项目: 国家自然科学基金(71901183);四川省科技厅应用基础研究(2021YJ0066)
详细信息
    作者简介:

    胡路(1985—),男,副教授,博士,研究方向为共享交通和城市轨道交通,E-mail:hulu@swjtu.edu.cn

  • 中图分类号: U491.2

Simulation Analysis on Influence of Congestion Propagation on Operation of Carsharing Systems

  • 摘要:

    随着共享汽车渗透率的不断增加,站点、路段层面的车辆溢出和拥挤传播现象日趋严重. 为刻画拥挤传播对汽车共享系统运行的影响机理,首先,搭建具有时变性和状态相关性的汽车共享系统排队网络;其次,基于C# 语言和O2DES离散事件仿真框架,提出并设计考虑车路交互影响和拥挤传播现象的汽车共享系统仿真模型,分析动态随机环境下站点与路段层面的拥挤传播现象对汽车共享系统运行的影响;最后,以成都市三站点的小规模汽车共享系统为例,在不同转运比例、需求和道路拥堵场景下,将该模型与引入虚拟空间的无穷排队模型进行对比分析. 研究结果表明:站点和路段层面的拥挤传播现象会导致系统服务率下降9.3%~16.9%,相比无穷排队模型,考虑拥挤传播现象的排队模型更能反映汽车共享系统的实际运营过程;当路网的道路占用率为70% (路网处于中度拥堵)时,考虑拥挤传播现象的汽车共享系统可实现最大收益;汽车共享系统的引入会为道路资源的动态分配带来新变化,当公共交通转向汽车共享系统的用户占比超过70%时,路网拥堵加剧,不利于汽车共享系统的有效运营和可持续发展.

     

  • 图 1  汽车共享排队网络拓扑结构

    Figure 1.  Carsharing queuing network topology

    图 2  站点模块事件触发关系

    Figure 2.  Event trigger diagram of station module

    图 3  路段模块事件触发关系

    Figure 3.  Event Trigger diagram of path module

    图 4  汽车共享系统站点位置

    Figure 4.  Location map of carsharing system stations

    图 5  三站点的汽车共享排队网络

    Figure 5.  Queuing network of carsharing within three stations

    图 6  系统状态和车辆平均速度

    Figure 6.  System state and average vehicle speed

    图 7  不同需求场景下的性能指标

    Figure 7.  Performance indicators under different demand scenarios

    图 8  不同拥堵场景下的性能指标

    Figure 8.  Performance indicators under different congestion scenarios

    图 9  不同转运比例下的性能指标

    Figure 9.  Performance indicators under different transfer ratios

    表  1  速度模型标准点取值

    Table  1.   Representative point values of velocity model

    相对密度平均行驶速度/(km•min−1
    ${\text{ } }{f_{m,{\text{p} } } }(t){\text{ = } }0$ ${v_{m,0,{\text{p} } } } = 1.00$
    ${f_{m,{\text{p} } } }(t) = {b_{m,1,{\text{p} } } }(t){\text{ = } }0.1$ ${v_{m,1,{\text{p} } } } = 0.55$
    ${f_{m,{\text{p} } } }(t) = {b_{m,2,{\text{p} } } }(t){\text{ = } }0.2$ ${v_{m,2,{\text{p} } } } = 0.40$
    下载: 导出CSV

    表  2  两种排队模型的性能指标求解结果

    Table  2.   Solved performance indicators of two queuing models

    模型系统服务率/%车辆利用率/%虚拟空间
    存储车辆
    占比率/%
    订单流失率/%利润/
    (元•d−1
    拥挤传播
    排队模型
    78.5023.78 0 21.50 4081.50
    无穷排队模型91.3331.09 34.28 8.67 5139.60
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-31
  • 修回日期:  2022-08-31
  • 网络出版日期:  2023-04-28
  • 刊出日期:  2022-09-22

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