Mechanism of Non-recurring Congestion Evolution Under Mixed
Traffic Flow with Connected and Autonomous Vehicles

doi:10.16097/j.cnki.1009-6744.2022.05.010

Abstract

Abstract: This paper investigates the interference between connected and autonomous vehicles (CAVs) and the traditional vehicles in mixed traffic flow, and proposed a model to describe the evolution of the non-recurring congestions in the mixed traffic flow. The model is developed based on the traditional traffic flow statistical theory model and the first-order continuous medium model, and introduced the intelligent driver model (IDM) and cooperative adaptive cruise control (CACC). The study uses the roadway section from Huatao Interchange to Banan Interchange in Chongqing City as a case study to examine the congestion evolution under different permeability ( Pc ) of CAVs in the traffic flow. The results show that higher penetration rate of CAVs relates to more significant improvement of the flow, occupancy, and speed of the mixed traffic flow. However, the improvement of congestion dissipation by CAVs is more obvious when Pc ≥ 0.2 . When Pc ≤ 0.8 , the duration of the congestion dissipation state with interference measure is approximately 50% of that without interference measure. When Pc = 1.0 , the traffic capacity of CAVs is 2.34 times of that in traffic flow with only traditional vehicles. The traffic congestion evaluation indexes are calculated under noninterference and interference measures and compared with the simulation results. The maximum relative error is within 5.38 %, which verified the model's accuracy. The research results provide important references for traffic congestion evaluations.

Key words: traffic engineering, traffic congestion, congestion evolution model, mixed traffic flow, connected vehicles

摘要： 针对混合交通流中智能网联车辆(Connected and Autonomous Vehicles, CAVs)和人工驾驶车辆的交织干涉问题，本文在传统交通流统计理论模型和一阶连续介质模型的基础上，通过引入智能驾驶员跟驰模型(Intelligent driver model, IDM)和协同自适应巡航控制模型(Cooperative Adaptive Cruise Control, CACC)，构建人工驾驶车辆和CAVs的混合交通流偶发拥堵演化模型，探索CAVs混入和诱导干涉措施对混合交通流偶发性拥堵传播规律的影响。实验选取重庆市华陶立交至巴南立交路段为路网原型，对CAVs不同渗透率( Pc )下的路段拥堵演化情况进行仿真。实验结果表明：CAVs渗透率越高，混合流流量、占有率和速度的改善情况越显著，但只有当 Pc ≥ 0.2 时，网联车辆对拥堵消散的改善效果才较为明显；Pc ≤ 0.8 时，干涉措施下，拥堵消散状态的持续时间约为不采用干涉措施的 50%；当 Pc = 1.0 时，网联车辆的通行能力是纯人工驾驶交通流的2.34倍；分别在非干涉措施和干涉措施下计算拥堵评价指标，与仿真结果进行对比，最大相对误差在5.38%之内，验证了模型的准确性。研究成果对疏散交通拥堵具有重要意义。

关键词: 交通工程, 交通拥堵, 拥堵演化模型, 混合交通流, 网联车辆

CLC Number:

U491

MA Qing-lu , NIU Sheng-ping , ZENG Hao-wei , DUAN Xue-feng. Mechanism of Non-recurring Congestion Evolution Under Mixed Traffic Flow with Connected and Autonomous Vehicles[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 97-106.

马庆禄, 牛圣平, 曾皓威, 段学锋. 网联环境下混合交通流偶发拥堵演化机理研究[J]. 交通运输系统工程与信息, 2022, 22(5): 97-106.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2022.05.010

http://www.tseit.org.cn/EN/Y2022/V22/I5/97

References

[1] 孙建平, 郭继孚, 张溪, 等. 基于速度变化的偶发性交通拥堵时空分布特性研究[J]. 交通运输系统工程与信息, 2019, 19(2): 196-201, 215. [SUN J P, GUO J F, ZHANG X, et al. Spatial and temporal distribution of occasional congestion based on speed variation[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(2): 196-201, 215.]

[2] 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.

[3] 秦严严, 王昊, 王炜, 等. 不同CACC渗透率条件下的混合交通流稳定性分析[J]. 交通运输系统工程与信息, 2017, 17(4): 63-69, 104. [QIN Y Y, WANG H, WANG W, et al. Mixed traffic flow string stability analysis for different CACC penetration ranges[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(4): 63-69, 104. ]

[4] 秦严严, 胡兴华, 何兆益, 等. CACC车头时距与混合交通流稳定性的解析关系[J]. 交通运输系统工程与信息, 2019, 19(6): 61-67. [QIN Y Y, HU X H, HE Z Y, et al. Analytical relationship between CACC headway and stability of mixed traffic flow[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(6): 61-67.]

[5] 王昊, 秦严严. 网联车混合交通流渐进稳定性解析方法 [J]. 哈尔滨工业大学学报, 2019, 51(3): 88- 91. [WANG H, QIN Y Y. Asymptotic stability analysis of traffic flow mixed with connected vehicles[J]. Journal of Harbin Institute of Technology, 2019, 51(3): 88-91.]

[6] 马庆禄, 傅宝宇, 曾皓威. 智能网联环境下异质交通流基本图和稳定性分析[J]. 交通信息与安全, 2021, 39 (5): 76-84. [MA Q L, FU B Y, ZENG H W. Fundamental diagram and stability analysis of heterogeneous traffic flow in a connected and autonomous environment[J]. Journal of Transport Information and Safety, 2021, 39(5): 76-84.]

[7] LAAN Z V, SCHONFELD P. Modeling heterogeneous traffic with cooperative adaptive cruise control vehicles: A first-order macroscopic perspective[J]. Transportation Planning and Technology, 2020, 43(2): 113-140.

[8] 徐桃让, 姚志洪, 蒋阳升, 等. 智能网联车环境下考虑反应时间影响的基本图模型[J]. 公路交通科技, 2020, 37(8): 108-117. [XU T R, YAO Z H, JIANG Y S, et al. Fundamental diagram model of considering reaction time in environment of intelligent connected vehicles[J]. Journal of Highway and Transportation Research and Development, 2020, 37(8): 108-117.]

[9] 伊振鹏, 李伟, 石白茜, 等. 协同自适应巡航控制车辆占比对下匝道分流区混合交通流安全性的影响分析 [J]. 交通信息与安全, 2022, 40(1): 10-18, 71. [YI Z P, LI W, SHI B X, et al. An impact analysis of the proportion of adaptive cruise control vehicles on the safety of mixed traffic flow at the off-ramp diverging area [J]. Journal of Transport Information and Safety, 2022, 40 (1): 10-18, 71. ]

[10] 李维佳, 王建军, 白骅, 等. 路网环境下考虑大型车混入率的事故疏散诱导模型[J]. 中国公路学报, 2020, 33 (11): 275- 284. [LI W J, WANG J J, BAI H, et al. An accident induction and evacuation system considering the mixing rate of the large vehicles under the freeway network[J]. China Journal of Highway and Transport, 2020, 33(11): 275-284.]

[11] 董长印, 王昊, 王炜, 等. 混入智能车的下匝道瓶颈路段交通流建模与仿真分析[J]. 物理学报, 2018, 67(14): 179-193. [DONG C Y, WANG H, WANG W, et al. Hybrid traffic flow model for intelligent vehicles exiting to off-ramp[J]. Acta Physica Sinica, 2018, 67(14): 179- 193.]

[12] TREIBER M, KESTING A. Traffic flow dynamics[M]. Berlin, Heidelberg: Springer, 2013.

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Mechanism of Non-recurring Congestion Evolution Under Mixed Traffic Flow with Connected and Autonomous Vehicles

网联环境下混合交通流偶发拥堵演化机理研究

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