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智能网联异质交通流混合特性

吴德华 彭锐 林熙玲

吴德华, 彭锐, 林熙玲. 智能网联异质交通流混合特性[J]. 西南交通大学学报, 2022, 57(4): 761-768. doi: 10.3969/j.issn.0258-2724.20210276
引用本文: 吴德华, 彭锐, 林熙玲. 智能网联异质交通流混合特性[J]. 西南交通大学学报, 2022, 57(4): 761-768. doi: 10.3969/j.issn.0258-2724.20210276
WU Dehua, PENG Rui, LIN Xiling. Hybrid Characteristics of Heterogeneous Traffic Flow in Intelligent Network[J]. Journal of Southwest Jiaotong University, 2022, 57(4): 761-768. doi: 10.3969/j.issn.0258-2724.20210276
Citation: WU Dehua, PENG Rui, LIN Xiling. Hybrid Characteristics of Heterogeneous Traffic Flow in Intelligent Network[J]. Journal of Southwest Jiaotong University, 2022, 57(4): 761-768. doi: 10.3969/j.issn.0258-2724.20210276

智能网联异质交通流混合特性

doi: 10.3969/j.issn.0258-2724.20210276
基金项目: 福建省自然科学基金(2016J01230)
详细信息
    作者简介:

    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: U495

Hybrid Characteristics of Heterogeneous Traffic Flow in Intelligent Network

  • 摘要:

    为研究车联网环境下异质交通流的演变规律,基于改进的NaSch模型,针对智能网联化程度的前期、中期和后期分别进行仿真实验,得到交通流基本图,并分析通行能力与网联车渗透率的内在联系;其次,通过马尔可夫链证明了网联车形成的有序排列能提高道路通行能力,随机仿真实验验证了理论推导的正确性;最后,引入考虑车辆排列方式的相对熵,从而定量描述异质车流的有序性,阐明了智能网联车辆(connected and autonomous vehicle,CAV)改善交通状况的本质原因. 研究结果表明:随着智能网联车渗透率的增加,通行能力增加,在智能网联化前期,渗透率的增加对通行能力提升较小,最高仅提升23.5%,中、后期通行能力最高能提升125.0%;在一定交通密度下,CAV渗透率与流量呈现正相关,相对熵与流量呈现负相关;智能网联车处于分离态时相对熵较小,分离态对随机混合的通行能力的提升随着CAV渗透率的增加而降低.

     

  • 图 1  智能网联程度三阶段

    Figure 1.  Three stages of intelligent network connectivity

    图 2  CAV与HV的排列方式

    Figure 2.  Arrangements of CAV and HV

    图 3  不同CAV与HV排列下的流量

    Figure 3.  Traffic flow under different arrangements of CAV and HV

    图 4  CAV-CAV频率

    Figure 4.  Frequency of CAV-CAV

    图 5  跟随行为频率

    Figure 5.  Frequency of following behavior

    图 6  CAV渗透率与相对熵关系

    Figure 6.  Relationship between CAV penetration and relative entropy

    图 7  CAV渗透率与流量关系

    Figure 7.  Relationship between CAV penetration and traffic flow

    图 8  相对熵与流量关系

    Figure 8.  Relationship between relative entropy and traffic flow

    图 9  随机态及分离态的流量

    Figure 9.  Traffic flows in random and separated states

    表  1  IDM参数取值

    Table  1.   Values of IDM parameters

    参数取值
    a/(m·s−2)1
    vf/(m·s−1)33.3
    $ s_{0} / \mathrm{m} $2
    $ T/{\rm{s}} $1.6,1.1
    b/(m·s−2)2
    L/m6
    下载: 导出CSV

    表  2  通行能力提升程度

    Table  2.   Level of capacity improvement %

    异质阶段渗透率
    0.20.40.60.81.0
    前期2.9 6.911.115.7 23.5
    中期7.318.831.552.0125.0
    后期2.712.032.055.0125.0
    下载: 导出CSV

    表  3  随机态和分离态的通行能力及相对熵

    Table  3.   Capacity and relative entropy in random and separated states

    渗透率相对熵/bit通行能力/(辆·h−1
    随机态分离态随机态分离态
    0.26.443.2219742461
    0.43.671.83 21622586
    0.62.041.02 22402750
    0.80.890.45 27452904
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-04-12
  • 修回日期:  2021-07-05
  • 刊出日期:  2021-07-09

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