Volume 42 Issue 2
Apr.  2024
Turn off MathJax
Article Contents
ZHANG Lu, ZHANG Zhaolei, LIU Zhizhen, TANG Feng. A Stability Analysis of Mixed Traffic Flows Considering Communication Delay of Connected and Autonomous Vehicles[J]. Journal of Transport Information and Safety, 2024, 42(2): 95-104. doi: 10.3963/j.jssn.1674-4861.2024.02.010
Citation: ZHANG Lu, ZHANG Zhaolei, LIU Zhizhen, TANG Feng. A Stability Analysis of Mixed Traffic Flows Considering Communication Delay of Connected and Autonomous Vehicles[J]. Journal of Transport Information and Safety, 2024, 42(2): 95-104. doi: 10.3963/j.jssn.1674-4861.2024.02.010

A Stability Analysis of Mixed Traffic Flows Considering Communication Delay of Connected and Autonomous Vehicles

doi: 10.3963/j.jssn.1674-4861.2024.02.010
  • Received Date: 2023-12-27
    Available Online: 2024-09-14
  • In traffic systems, minor disturbances can significantly destabilize traffic flow and cause vehicles to exhibit frequent start-stop problem. This study aims to identify disturbance suppression techniques for mixed traffic flows, taking into account the issue of communication delay of connected and autonomous vehicle (CAV). To study the influence mechanism of vehicle communication on the stability of mixed traffic flows, a comprehensive analysis of multiple factors such as the delay of communication among CAV, the market penetration of CAV and the platooning intensity is conducted. Considering the impact of maximum size of CAV platooning, a stability analysis model of mixed traffic flows is constructed based on Markov chain, which can derive the generation probability of different headway types. On such basis, the stability identification formula of mixed traffic flows is developed to analyze the stable speed range under different conditions. In order to improve the efficiency of communication among CAV, the roadside units are used to transmit information. According to the different directions of vehicle-road communication, the communication process is divided into uplink communication from vehicle-to-road and downlink communication from road-to-vehicle. Next, the communication delay estimation model under low traffic density is developed, based on which the communication delay under different CAV penetrations and its platooning intensities to analyze its impact on traffic flow stability. To validate the analytical results, simulation experiments of disturbance evolution are conducted. The results indicate that: ①The market penetration of CAV and its platooning intensity are beneficial to the stability of mixed traffic flow. ② Communication delay has a negative impact on the stability of mix traffic flow. In detail, the delay decreases with the increase of the market penetration of CAV and its platooning intensity, the coverage of roadside units and CAVs'communication radius.③ When the maximum size of CAV platoon equaling 6 and the steady speed of mixed flow travels equaling 25 m/s, only when the market penetration of CAV reaches 60% or its platooning intensity is greater than 0.5, can the delay be less than 0.3 s and the disturbance

     

  • loading
  • [1]
    GYAWALI S, XU S, QIAN Y, et al. Challenges and solutions for cellular based V2X communications[J]. IEEE Communications Surveys & Tutorials, 2021, 23(1): 222-255.
    [2]
    蒋阳升, 胡蓉, 姚志洪, 等. 智能网联车环境下异质交通流稳定性及安全性分析[J]. 北京交通大学学报, 2020, 44(1): 27-33.

    JIANG Y S, HU R, YAO Z H, et al. Stability and safety analysis for heterogeneous traffic flow composed of intelligent and connected vehicles[J]. Journal of Beijing Jiaotong University, 2020, 44(1): 27-33. (in Chinese)
    [3]
    JIN S, SUN D H, ZHAO M, et al. Modeling and stability analysis of mixed traffic with conventional and connected automated vehicles from cyber physical perspective[J]. Physica A: Statistical Mechanics and its Applications, 2020, 551(1): 124217-124230.
    [4]
    吴兵, 王文璇, 李林波, 等. 多前车影响的智能网联车辆纵向控制模型[J]. 交通运输工程学报, 2020, 20(2): 184-194.

    WU B, WANG W X, LI L B, et al. Longitudinal control model for connected autonomous vehicles influenced by multiple preceding vehicles[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 184-194. (in Chinese)
    [5]
    QIN Y Y, WANG H. Stabilizing mixed cooperative adaptive cruise control traffic flow to balance capacity using car-following model[J]. Journal of Intelligent Transportation Systems, 2023, 27(1): 57-79. doi: 10.1080/15472450.2021.1985490
    [6]
    单肖年, 万长薪, 李志斌, 等. 智能网联环境下多车道异质交通流建模与仿真[J]. 交通运输系统工程与信息, 2022, 22 (6): 74-84.

    SHAN X N, WAN C X, LI Z B, et al. Modeling and simulation of multi-lane heterogeneous traffic flow in intelligent and connected vehicle environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 74-84. (in Chinese)
    [7]
    ZHOU J, ZHU F. Analytical analysis of the effect of maximum platoon size of connected and automated vehicles[J]. Transportation Research Part C: Emerging Technologies, 2021, 122(1): 102882-1028102.
    [8]
    ZHANG Y C, ZHAO M, SUN D H, et al. Analysis of mixed traffic with connected and non-connected vehicles based on lattice hydrodynamic model[J]. Communications in Nonlinear Science & Numerical Simulation, 2021, 94(4): 105541-105562.
    [9]
    YAO Z, JIANG Y, HU R, et al. Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways[J]. Journal of Safety Research, 2020, 75(1): 262-274.
    [10]
    CHANG X, LI H, RONG J, et al. Analysis on traffic stability and capacity for mixed traffic flow with platoons of intelligent connected vehicles[J]. Physica A: Statistical Mechanics and its Applications, 2020, 557(1): 124829-124842.
    [11]
    GHIASI A, HUSSAIN O, QIAN Z S, et al. A mixed traffic capacity analysis and lane management model for connected automated vehicles: a Markov chain method[J]. Transportation Research Part B: Methodological, 2017, 106(12): 266-292.
    [12]
    LI L, GAN J, QU X, et al. Stability and environmental analysis of mixed traffic flow-using the Markov probabilistic theory[J]. Promet-Traffic & Transportation, 2020, 32(6): 849-861.
    [13]
    秦严严, 罗钦中, 贺正冰. 网联自动驾驶车辆混合交通流专用道管控方法[J]. 交通运输工程学报, 2023, 23(3): 221-231.

    QIN Y Y, LUO Q Z, HE Z B. Management and control method of dedicated lanes for mixed traffic flows with connected and automated vehicles[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 221-231. (in Chinese)
    [14]
    秦严严, 陈凌志. 混有智能辅助驾驶车队的混合车流通行能力分析[J]. 重庆交通大学学报(自然科学版), 2022, 41 (12): 1-10.

    QIN Y Y, CHEN L Z. Traffic capacity of traffic flow mixed with intelligent assistant driving vehicle platoons[J]. Journal of Chongqing Jiaotong University(Natural Science), 2022, 41(12): 1-10. (in Chinese)
    [15]
    NGODUY D, LI T. Hopf bifurcation structure of a generic car-following model with multiple time delays[J]. Transportmetrica A: Transport Science, 2021, 17(4): 878-896.
    [16]
    李永福, 何昌鹏, 朱浩, 等. 通信延时环境下异质网联车辆队列非线性纵向控制[J]. 自动化学报, 2021, 47(12): 2841-2856.

    LI Y F, HE C P, ZHU H, et al. Nonlinear longitudinal control for heterogeneous connected vehicle platoon in the presence of communication delays[J]. Acta Automatica Sinica, 2021, 47(12): 2841-2856. (in Chinese)
    [17]
    JIA D, NGODUY D, VU H. A multiclass microscopic model for heterogeneous platoon with vehicle-to-vehicle communication[J]. Transportmetrica B, 2019, 7(1): 311-335.
    [18]
    JIA D, NGODUY D. Enhanced cooperative car-following traffic model with the combination ofV2VandV2I communication[J]. Transportation Research Part B: Methodological, 2016, 90(1): 172-191.
    [19]
    JIA D, NGODUY D. Platoon based cooperative driving model with consideration of realistic inter-vehicle communication[J]. Transportation Research Part C: Emerging Technologies, 2016, 68(1): 245-264.
    [20]
    于冲, 赵海, 司帅宗, 等. 考虑通信延时的platoon跟车控制模型[J]. 控制与决策, 2019, 34(2): 377-382.

    YU C, ZHAO H, SI S Z, et al. Vehicle following control model of platoon considering communication delay[J]. Control and Decision, 2019, 34(2): 377-382. (in Chinese)
    [21]
    GUO C, LI D, CHEN X, et al. An adaptive V2R communication strategy based on data delivery delay estimation in VANETs[J]. Vehicular Communications, 2022, 34(1): 100444-100457.
    [22]
    叶青, 赵聪, 朱逸凡, 等. 面向自动驾驶的车路协同感知点云融合模式时延影响分析[J]. 交通信息与安全, 2023, 41 (4): 72-79.

    YE Q, ZHAO C, ZHU Y F, et al. An analysis of the impact of time delay of fusion modes for point clouds from cooperative road vehicle systems on autonomous driving[J]. Journal of Transport Information and Safety, 2023, 41(4): 72-79. (in Chinese)
    [23]
    MILANES V, SHLADOVER S E. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data[J]. Transportation Research Part C: Emerging Technologies, 2014, 48(1): 285-300.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(1)

    Article Metrics

    Article views (140) PDF downloads(3) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return