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异质流网联车的不同换道集聚策略

吴德华 彭锐 陈荣峰

吴德华, 彭锐, 陈荣峰. 异质流网联车的不同换道集聚策略[J]. 西南交通大学学报, 2023, 58(2): 348-356. doi: 10.3969/j.issn.0258-2724.20211035
引用本文: 吴德华, 彭锐, 陈荣峰. 异质流网联车的不同换道集聚策略[J]. 西南交通大学学报, 2023, 58(2): 348-356. doi: 10.3969/j.issn.0258-2724.20211035
WU Dehua, PENG Rui, CHEN Rongfeng. Hybrid Characteristics of Heterogeneous Traffic Flow Under Different Aggregating Lane-Change Strategies in Intelligent Network[J]. Journal of Southwest Jiaotong University, 2023, 58(2): 348-356. doi: 10.3969/j.issn.0258-2724.20211035
Citation: WU Dehua, PENG Rui, CHEN Rongfeng. Hybrid Characteristics of Heterogeneous Traffic Flow Under Different Aggregating Lane-Change Strategies in Intelligent Network[J]. Journal of Southwest Jiaotong University, 2023, 58(2): 348-356. doi: 10.3969/j.issn.0258-2724.20211035

异质流网联车的不同换道集聚策略

doi: 10.3969/j.issn.0258-2724.20211035
基金项目: 福建省自然科学基金(2016J01230)
详细信息
    通讯作者:

    吴德华(1978—),男,副教授,博士,研究方向为智能交通,E-mail:610706517@qq.com

  • 中图分类号: U268.6

Hybrid Characteristics of Heterogeneous Traffic Flow Under Different Aggregating Lane-Change Strategies in Intelligent Network

Funds: QIN Yanyan, WANG Hao, WANG Wei, et al. Stability analysis and fundamental diagram of heterogeneous traffic flow mixed with cooperative adaptive cruise control vehicles[J]. Acta Physica Sinica, 2017, 66(9): 257-265.
  • 摘要:

    为研究车联网环境下异质交通流的演变规律,首先,引入相对熵定量描述异质流的有序性,并分析有序性与智能网联车(connected and autonomous vehicle,CAV)市场渗透率、协同自适应巡航控制(cooperative adaptive cruise control,CACC)队列数之间的内在联系,推导得出智能网联车渗透率的增加及队列数的减少可以提升异质流的有序性;其次,提出了保守型集聚(conservative aggregation,CSA)、激进型集聚(radical aggregation,RDA)两种改进的智能网联车集聚换道策略,并通过元胞自动机仿真实验,从通行能力、相对熵和平均队列长度等方面比较了无集聚(no aggregation,NOA)、常规集聚(conventional aggregation,CVA)、CSA、RDA 4种换道策略的优劣;最后,在CSA换道策略中分析了不同最小队列规模限制对于通行能力的影响. 研究结果表明:在双车道环境下,采取集聚换道策略能使智能网联车形成CACC队列,使异质流趋于“有序”,在20~95辆/km密度范围内提升通行能力;相比NOA换道策略,CSA、RDA换道策略分别最大提升道路通行能力12.6%、14.0%,但当智能网联车市场渗透率为0.8时,RDA换道策略导致最大通行能力反而降低25.8%;根据相对熵对异质流中智能网联车辆集聚程度的定量描述,NOA、CVA、CSA、RDA 4种换道策略对智能网联的集聚能力依次递增;在CSA换道策略中,CACC最小队列规模取4辆时道路通行效率达到最佳.

     

  • 图 1  CAV排列方式

    Figure 1.  Arrangement of CAV

    图 2  CVA控制算法流程

    Figure 2.  Flowchart of CVA control algorithm

    图 3  CAV换道示意

    Figure 3.  CAV lane change

    图 4  CSA控制算法流程

    Figure 4.  Flowchart of CSA control algorithm

    图 5  RDA控制算法流程

    Figure 5.  Flowchart of RDA control algorithm

    图 6  不同渗透率下流量与密度关系

    Figure 6.  Relationship between flow and density under different penetration rates

    图 7  最大通行能力

    Figure 7.  Maximum capacity

    图 8  不同换道策略下相对熵

    Figure 8.  Relative entropy under each lane change strategy

    图 9  不同策略下的队列规模

    Figure 9.  Queue sizes under different strategies

    图 10  两车道密度差

    Figure 10.  Density difference between two lanes

    图 11  规模限制与最大流量关系

    Figure 11.  Relationship between size limit and maximum flow

    表  1  仿真参数值

    Table  1.   Simulation parameter value

    参数取值
    ${a / \rm{ {(m {\text{•} } {s^{ - 2} } })} }$1
    $ {{{v_{\rm{f}}}}/ {({\rm{m}}}} {\text{•}} {{\rm{s}}^{ - {\text{1}}}}) $33.3
    $ {{{v_{{\rm{max}}}}} / {({\rm{m}}}} {\text{•}} {{\rm{s}}^{ - {\text{1}}}}) $33.3
    $ {{{d_0}} / {\rm{m}}} $2
    $ {{{t_0}} /{\rm{ s}}} $1.6 (CAV), 1.1 (HV)
    $ {b / {({\rm{m}}}} {\text{•}}{{\rm{s}}^{ - 2}}) $2
    $ {{{b_{{\rm{max}}}}}/ {({\rm{m}}}} {\text{•}} {{\rm{s}}^{ - 2}}) $6
    $ {l / {\rm{m}}} $6
    $ {{{D_{\rm{R}}}} / {\rm{m}}} $300
    $ {k_{\rm{p}}} $0.45
    $ {k_{\rm{d}}} $0.25
    $ {t_{\rm{c}}} $0.6
    $ \lambda $0.5
    Smin/辆3.0
    下载: 导出CSV

    表  2  最大通行能力提升程度

    Table  2.   Maximum capacity improvement %

    集聚策略p = 0.2p = 0.4p = 0.6p = 0.8
    CVA2.16.71.20.4
    CSA10.412.65.13.3
    RDA12.314.09.7−25.8
    下载: 导出CSV
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    QIN Yanyan, WANG Hao, WANG Wei, et al. Stability analysis and fundamental diagram of heterogeneous traffic flow mixed with cooperative adaptive cruise control vehicles[J]. Acta Physica Sinica, 2017, 66(9): 257-265. doi: 10.7498/aps.66.094502
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出版历程
  • 收稿日期:  2021-12-12
  • 修回日期:  2022-06-30
  • 网络出版日期:  2023-01-18
  • 刊出日期:  2022-07-12

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