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融入公交车与自动驾驶车队的异质交通流模型

梁军 耿浩然 陈龙 于滨 鲁光泉

梁军, 耿浩然, 陈龙, 于滨, 鲁光泉. 融入公交车与自动驾驶车队的异质交通流模型[J]. 西南交通大学学报, 2023, 58(5): 1090-1099. doi: 10.3969/j.issn.0258-2724.20220313
引用本文: 梁军, 耿浩然, 陈龙, 于滨, 鲁光泉. 融入公交车与自动驾驶车队的异质交通流模型[J]. 西南交通大学学报, 2023, 58(5): 1090-1099. doi: 10.3969/j.issn.0258-2724.20220313
LIANG Jun, GENG Haoran, CHEN Long, YU Bin, LU Guangquan. Integrated Heterogeneous Traffic Flow Model of Bus and Autonomous Vehicle Platoon[J]. Journal of Southwest Jiaotong University, 2023, 58(5): 1090-1099. doi: 10.3969/j.issn.0258-2724.20220313
Citation: LIANG Jun, GENG Haoran, CHEN Long, YU Bin, LU Guangquan. Integrated Heterogeneous Traffic Flow Model of Bus and Autonomous Vehicle Platoon[J]. Journal of Southwest Jiaotong University, 2023, 58(5): 1090-1099. doi: 10.3969/j.issn.0258-2724.20220313

融入公交车与自动驾驶车队的异质交通流模型

doi: 10.3969/j.issn.0258-2724.20220313
基金项目: 国家重点研发计划(2018YFB0105003);国家自然科学基金(51875225)
详细信息
    作者简介:

    梁军(1976—),男,教授,博士,研究方向为智能车辆与智能交通,E-mail:liangjun@ujs.edu.cn

  • 中图分类号: U491.25

Integrated Heterogeneous Traffic Flow Model of Bus and Autonomous Vehicle Platoon

  • 摘要:

    为研究网联自动驾驶车(connected autonomous vehicle, CAV)和人工驾驶车(human-pilot vehicle, HPV)所组成的异质交通流特性及公交车驾驶行为对环境的影响,首先,分析异质交通流中的4种跟驰模式:人工驾驶小汽车跟驰、人工驾驶公交车跟驰、自适应巡航控制(adaptive cruise control, ACC)跟驰和协同自适应巡航控制(cooperative adaptive cruise control, CACC)跟驰;接着,基于各跟驰模型的特点,构建车辆跟驰和换道的元胞自动机模型,综合考虑CAV车队特性、驾驶员与CAV各自反应时间特性以及HPV加塞特性,并利用跟驰模式判断参数融合不同跟驰模式特性,实现统一的模型表达;最后,仿真分析不同CAV渗透率下CAV排队强度及公交车换道行为对交通流的影响. 结果表明:在一定的CAV渗透率下,促使CAV形成队列比单纯提高CAV渗透率更能有效提升道路通行效率;适量的公交换道有助于充分利用道路通行能力,过多的公交换道则会妨碍正常交通,公交换道对交通流造成的通行效率衰减随CAV渗透率的增大而减小;同步流状态下,人工驾驶小汽车执行率与道路通行效率呈负相关关系;而在堵塞流状态下,人工驾驶小汽车执行率对通行效率影响甚微.

     

  • 图 1  跟驰类型示意

    Figure 1.  Schematic of car following type

    图 2  小汽车、公交车安全距离

    Figure 2.  Safe distances for cars and buses

    图 3  换道过程

    Figure 3.  Lane-changing process

    图 4  CAV换道场景

    Figure 4.  CAV lane-changing scenario

    图 5  加塞场景示意

    Figure 5.  Cutting-in scenario

    图 6  不同CAV渗透率与排队强度下的时空图

    Figure 6.  Spatio-temporal diagram with different CAV permeabilities and queuing intensities

    图 7  公交车换道流程

    Figure 7.  Process of bus lane change

    图 8  公交车换道次数-交通流量示意

    Figure 8.  Bus lane-changing times versus traffic flow

    图 9  不同CAV渗透率下交通流均值及标准差

    Figure 9.  Mean and standard deviation of traffic flow under different CAV permeabilities

    图 10  交通流量与公交车换道次数相关性热图

    Figure 10.  Heat map of correlation between traffic flow and number of bus lane-changing times

    图 11  不同驾驶员换道意向下车流平均速度

    Figure 11.  Average traffic speed under lane-changing intentions from different drivers

    图 12  HPV车辆遭遇公交车换道决策过程

    Figure 12.  Decision-making process of HPV vehicle encountering bus lane change

    图 13  不同CAV渗透率下4种HPV执行率对应的密度-车辆平均延误

    Figure 13.  Density versus vehicle average delay with four types of HPV execution rates at different CAV permeabilities

    表  1  参数说明

    Table  1.   Parameter descriptions

    参数符号参数说明
    $ {\alpha _n} $  ${\alpha _n} \in \left\{ {0,1} \right\}$,1 表示 ACC 跟驰模式,0 表示非ACC 跟驰模式
    $ {\beta _n} $  $ {\beta _n} \in \left\{ {0,1} \right\}$,1 表示 CACC 跟驰模式,0 表示非 CACC 跟驰模式
    $ {\gamma _n} $  $ {\gamma _n} \in \left\{ {0,1} \right\}$,1 表示 HPVc 跟驰模式,0 表示非HPVc 跟驰模式跟驰
    Ωn  ${\varOmega _n} \in \left\{ {0,1} \right\}$,1 表示 HPVb 跟驰模式,0 表示非HPVb 跟驰模式跟驰
    $ {v_n}\left( t \right) $  时刻 t 车辆 n 的速度,n+1为前车,n−1为后车
    $ {x_n}\left( t \right) $  时刻 t 车辆 n 的位置,n+1为前车,n−1为后车
    ${ v_{ {\rm{B} },n + 1}( t)}$  时刻 t 车辆 n 在车道 B 前车速度
    $ {d_n} $  车辆 n 与前车车间距
    $ d_n^{{\text{safe}}} $  车辆 n 的安全车距
    $ {a_n} $  车辆 n 的加速度
    $ \Delta t $  仿真实验时间步长
    $ {\tau _j} $  人类驾驶员与自动驾驶车的反应时间,$j\in ${HPV,CAV}
    $v_n^{{\rm{\max }}}$  车辆 n 的最大速度
    $ {b_i} $  $ i \in \{ {\text{car,bus} }\}$,依次表示小汽车(car)与公交车(bus)的最大减速度
    ${m_{\rm{A} } } ( {m_{\rm{B} } }) $  在 CAV 通信范围内车道 A (车道 B)能获取信息的前方车辆数
    ${m_{{\rm{\max }}} }$  CAV 通信范围内可获取间隔为安全距离的最大前方车辆数$ ({m_{{\rm{A}}}},m_{\rm{B}} \leqslant {m_{\max }}) $
    ${\tilde d_{{\rm{B}},n} }$  车道 B 上车辆 n 对应相同位置车头与前车车距
    $ {d_{{\rm{B}},n + m}} $  在车道 B 上车辆 n 前方第 m 辆车与其前车间的车距
    $ C\left( x \right) $  $ x$位置所在元胞状态,0 表示有车,1 表示无车
    $ {l_n} $  车辆 n 的长度
    ${l_{\rm{c}}}$  元胞长度
    $ O $  CAV 排队强度
    $ {q_n} $  车辆 n 占用元胞数
    ${p_{ {\text{random} } } }$  随机慢化概率
    ${p_{\rm{g}}}$  车辆换道产生加塞情况概率
    下载: 导出CSV

    表  2  仿真参数设置

    Table  2.   Simulation parameter setting

    参数符号仿真值
    $a_n/(元胞{\text{•}}时间步^{-2})$$ {a_{{\text{car}}}} = 2 $,$ {a_{{\text{bus}}}} = 1 $
    $ {\tau _{{\text{HPV}}}}/{\rm{s}} $1.5
    ${\tau _{ {\text{CAV} } } } /{\rm{s}}$0.5
    ${b_{ {\text{car} } } } $/(元胞·时间步−2−4
    $ {b_{{\text{bus}}}} $/(元胞·时间步−2−3
    $ {m_{\max }} $50
    $ {p_{{\text{random}}}} $,${p_{ {\rm{g} } } }$0.5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-28
  • 修回日期:  2022-09-08
  • 网络出版日期:  2023-05-12
  • 刊出日期:  2022-09-21

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