Integrated Heterogeneous Traffic Flow Model of Bus and Autonomous Vehicle Platoon
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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.
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.
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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
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 
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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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