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

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

梁军^1,,
耿浩然^1,,
陈龙¹,
于滨²,
鲁光泉²

1.
江苏大学汽车工程研究院，江苏镇江 212013
2.
北京航空航天大学交通科学与工程学院，北京 100191

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
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- 被引次数: 7
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

Integrated Heterogeneous Traffic Flow Model of Bus and Autonomous Vehicle Platoon

1.
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
2.
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China

摘要

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.
- 交通运输工程 /
- 元胞自动机 /
- 异质交通流 /
- 公交车换道 /
- 排队强度
Abstract:
In order to investigate the heterogeneous traffic flow characteristics formed by the combination of connected autonomous vehicles (CAVs) and human-pilot vehicles (HPVs), as well as the impact of bus driving behavior on this environment, four following modes in heterogeneous traffic flow are initially analyzed: human-pilot car following, human-pilot bus following, adaptive cruise control (ACC) following, and cooperative adaptive cruise control (CACC) following. Subsequently, based on the characteristics of each following model, a cellular automaton model for vehicle following and lane-changing is constructed, which comprehensively considers the characteristics of CAV platoons, the response time characteristics of drivers and CAVs, and the cutting-in behavior of HPVs. By setting the following mode judgment parameters, different following mode characteristics are integrated to achieve a unified model representation. Lastly, through simulation experiments, the queuing intensity of CAVs at different penetration rates and the impact of bus lane-changing behavior on traffic flow are analyzed. The results indicate that promoting CAVs to form platoons at a certain penetration rate is more effective in improving road traffic efficiency than merely increasing the CAV penetration rate; a moderate amount of bus lane-changing contributes to the full utilization of road traffic capacity, while excessive bus lane-changing hinders normal traffic. The traffic efficiency attenuation caused by bus lane-changing decreases as the CAV penetration rate increases; under synchronized flow conditions, the execution rate of human-pilot cars is negatively correlated with road traffic efficiency; however, under congested flow conditions, the impact of human-pilot car execution rate on traffic efficiency is negligible.
- traffic engineering /
- cellular automaton /
- heterogeneous traffic flow /
- bus lane-changing behavior /
- queuing intensity

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图 1 跟驰类型示意

Figure 1. Schematic of car following type

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图 2 小汽车、公交车安全距离

Figure 2. Safe distances for cars and buses

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图 3 换道过程

Figure 3. Lane-changing process

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图 4 CAV换道场景

Figure 4. CAV lane-changing scenario

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图 5 加塞场景示意

Figure 5. Cutting-in scenario

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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

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图 7 公交车换道流程

Figure 7. Process of bus lane change

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图 8 公交车换道次数-交通流量示意

Figure 8. Bus lane-changing times versus traffic flow

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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

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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

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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

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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

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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

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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 HPV_c 跟驰模式，0 表示非HPV_c 跟驰模式跟驰
Ω_n	${\varOmega _n} \in \left\{ {0,1} \right\}$ ，1 表示 HPV_b 跟驰模式，0 表示非HPV_b 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

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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

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