混合态与分离态异质交通流稳定性分析

doi:10.16097/j.cnki.1009-6744.2025.03.017

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (3): 190-203.DOI: 10.16097/j.cnki.1009-6744.2025.03.017

混合态与分离态异质交通流稳定性分析

张文会^*，宋子文，张超，席聪

东北林业大学，土木与交通学院，哈尔滨150040

收稿日期:2025-02-10 修回日期:2025-03-21 接受日期:2025-03-27 出版日期:2025-06-25 发布日期:2025-06-20
作者简介:张文会(1978—)，男，黑龙江哈尔滨人，教授，博士。
基金资助:
国家自然科学基金(51638004)。

Stability Analysis for Heterogeneous Traffic Flow in Mixed State and Separated State

ZHANG Wenhui^*, SONG Ziwen, ZHANG Chao, XI Cong

School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China

Received:2025-02-10 Revised:2025-03-21 Accepted:2025-03-27 Online:2025-06-25 Published:2025-06-20
Supported by:
National Natural Science Foundation of China (51638004)。

摘要/Abstract

摘要： 为揭示人工驾驶车辆(HDV)和智能网联车辆(CAV)构成的异质交通流的稳定性演化规律，从其混合态与分离态两种排列方式入手展开研究。首先，考虑CAV功能退化现象，构建适合异质交通流的改进跟驰模型和换道模型，建立各车型数量比例的期望表达式；其次，通过推导混合态与分离态的基本图模型，解析高峰与平峰时段的流量-密度曲线特征差异；之后，理论推导异质交通流稳定性判别条件，并分析队列规模对稳定性的影响；最后，设计数值仿真实验，验证扰动条件下的车流稳定性，对比分析分离态与混合态下行驶速度和CAV渗透率对速度扰动幅度的影响。结果表明：在相同行驶速度下，分离态实现车流稳定所需CAV渗透率较混合态平均降低12.7%，其稳定性随队列规模扩大呈现非线性增长，并可使高峰时段最大流量提升14.7%。但当CAV渗透率低于0.2时，混合态的车流抗扰动能力更强；当行驶速度处于15，25，35 m·s^-1下，CAV渗透率将分别达到0.86、0.71和0.47，此时，采用分离态和混合态均可以在车流扰动中保持稳定；在高速工况下，混合态与分离态达到稳定性所需的CAV渗透率差异将缩短至0.0236。

关键词: 智能交通, 基本图与稳定性, 数值仿真, 异质交通流, 队列规模

Abstract: To elucidate the stability evolution mechanisms of heterogeneous traffic flows composed of Human Driven Vehicle (HDV) and Connected and Autonomous Vehicle (CAV), the study investigates their dynamic behaviors under mixed and separated configurations. Considering CAV functional degradation, we developed improved car-following and lane-changing models suitable for heterogeneous traffic flow, establishing an expected expression for the relative quantity ratio of different vehicle types. Fundamental diagram models for both states were derived, with analytical characterization of flow-density curve disparities between peak and off-peak periods. Stability criteria were theoretically established, and the influence of platoon size on stability was systematically analyzed. Finally, numerical simulations validated traffic flow stability under perturbations, with comparative analysis of the effect of velocity disturbance amplitude through traffic speeds and CAV penetration rates of interactions in both states. The results indicate that the separated-state reduces the critical CAV penetration rate by 12.7% on average compared to the mixed-state at identical speeds, exhibiting nonlinear stability growth with platoon expansion and enhancing peak-hour capacity by 14.7%. Mixed-state demonstrates superior disturbance resistance when CAV penetration rate falls below 0.2. CAV penetrations rate of 0.86, 0.71, and 0.47 at 15 m·s^-1, 25 m·s^-1, and 35 m·s^-1 respectively, with both states maintaining stability under perturbations. At high-speed conditions, the required CAV penetration rate difference between the two states narrows to 0.0236.

Key words: intelligent transportation, fundamental diagram and stability, numerical simulation, heterogeneous traffic flow, platoon size

中图分类号:

U491.2+6

张文会, 宋子文, 张超, 席聪. 混合态与分离态异质交通流稳定性分析[J]. 交通运输系统工程与信息, 2025, 25(3): 190-203.

ZHANG Wenhui, SONG Ziwen, ZHANG Chao, XI Cong. Stability Analysis for Heterogeneous Traffic Flow in Mixed State and Separated State[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(3): 190-203.

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参考文献

[1] ZHENGY, RANB, QUX, et al. Cooperative lane changing strategies to improve traffic operation and safety nearby freeway off-ramps in a connected and automated vehicles environment[J]. IEEETransactions on Intelligent Transportation Systems, 2020, 21(11): 4605-4614.

[2] YAOZ,XUT,JIANGY,etal.Linearstabilityanalysisof heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time[J]. PhysicaA: StatisticalMechanics and itsApplications, 2021,561:125218.

[3]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.]

[4]李霞,汪一戈,崔洪军,等.智能网联环境下复杂异质交通流稳定性解析[J]. 交通运输系统工程与信息,2020, 20(6): 114-120. [LI X, WANG Y G, CUI H J, et al. Stability analysis of complex heterogeneous traffic flow under connected and autonomous environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(6): 114-120.]

[5]杜文举,赵尚飞,李引珍,等.考虑前后多车的混合交通流稳定性与安全性分析[J].交通运输系统工程与信息, 2024, 24(6): 206-218. [DU W J, ZHAO S F, LI Y Z, et al. Stability and safety analysis of mixed traffic flow considering multiple preceding and following vehicles[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(6): 206-218.]

[6]GAO Z, WANG J, ZHANG X, et al. Traffic oscillations mitigation in vehicle platoon using a car-following control model for connected and autonomous vehicle[J]. Journal of Advanced Transportation, 2019, 2019: 3067291.

[7]YAO Z, WU Y, WANG Y, et al. Analysis of the impact of maximum platoon size of CAVs on mixed traffic flow: An analytical and simulation method[J]. Transportation Research Part C: Emerging Technologies, 2023, 147: 103989.

[8]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: 102882.

[9]赵尚飞,杜文举.多类时延下复杂混合交通流基本图与稳定性分析[J]. 计算机工程与应用, 2025, 61(3): 336-348. [ZHAO S F, DU W J. Fundamental diagram and stability analysis for complex mixed traffic flow considering multiclass time-delay[J]. Computer Engineering and Applications, 2025, 61(3): 336-348.]

[10] XIE D F, ZHAO X M, HE Z. Heterogeneous traffic mixing regular and connected vehicles: Modeling and stabilization[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(6): 2060-2071.

[11] MILANÉS 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: 285-300.

[12] 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. [WU D H, PENG R, LIN X L. Hybrid characteristics of heterogeneous traffic flow in intelligent network[J]. Journal of Southwest Jiaotong University, 2022, 57(4): 761-768.]

[13] 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.]

[14] YAO Z H, LI L, LIAO W B, et al. Optimal lane management policy for connected automated vehicles in mixed traffic flow[J]. Physica A: Statistical Mechanics and its Applications, 2024, 637: 129520.

[15] WANG H, WAN Q, WANG W. Asymptotic stability analysis of binary heterogeneous traffic based on car following model[J]. Discrete Dynamics in Nature and Society, 2016, 2016(1): 3480368.

混合态与分离态异质交通流稳定性分析

Stability Analysis for Heterogeneous Traffic Flow in Mixed State and Separated State

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