Modeling and Simulation of Multi-lane Heterogeneous Traffic
Flow in Intelligent and Connected Vehicle Environment

doi:10.16097/j.cnki.1009-6744.2022.06.008

Abstract

Abstract: To explore the operation characteristics of multi-lane heterogeneous traffic flow in mixed Connected and Automated Vehicle (CAV) and Human Driving Vehicle (HDV) environment, this paper analyzes the car-following modes of CAVs and HDVs in heterogeneous traffic flow and proposes two-lane and multi-lane changing models for different vehicle types. The paper establishes a multi-lane heterogeneous traffic flow simulation model and then analyzes the road capacity and lane-changing behavior characteristics under different CAV market penetration rates. The results indicate that with the increase in CAV market penetration rate, the single-lane road capacity increases from 1678 pcu · h-1 to 4200 pcu · h-1 , the critical density changes from 25 pcu · km-1 to 35 pcu · km-1 , which show significant differences for different number of lanes. It is also found that the lane-changing behavior of heterogeneous traffic flow has three-stage characteristics. At low density, vehicles can drive or change lanes freely. When the density is between 20~100 pcu·km-1 , vehicle lane-changing frequency overall follows a convex curve. With the CAV penetration rate increases, the peak value of HDV sees an increase trend, while the peak value of CAV is decreasing. Under highdensity, due to the constraints of available lane-changing space, vehicles cannot complete lane-changing behavior. The benefits of lane-changing behavior are further discussed, with the indicators of the increment of traffic volume and order improvement. The study results help to understand the operation status of multi-lane heterogeneous traffic flow and provides theoretical references for the future management of heterogeneous traffic flow.

Key words: traffic engineering, heterogeneous traffic flow, road capacity, lane changing model, intelligent and connected vehicle environment

摘要： 为探究智能网联自动驾驶车辆(Connected and Autonomous Vehicle, CAV)与人工驾驶车辆 (Human Driving Vehicle, HDV)混合行驶的多车道异质交通流运行特征，本文剖析了异质交通流中不同类型车辆的跟驰模式，提出不同类型车辆双车道及多车道换道模型，进而构建了多车道异质交通流仿真模型，并分析了不同CAV混入率下的道路通行能力及换道行为特征。研究结果表明，随着CAV渗透率的提高，单车道通行能力由1678 pcu·h-1提升至4200 pcu·h-1，交通流临界密度由25 pcu·km-1增长至35 pcu·km-1 ，同一渗透率下不同车道数的道路通行能力及临界密度值呈现显著差异性。异质交通流换道行为呈现三阶段特征：在低密度下，不同类型车辆均可自由行驶及换道；密度在20~100 pcu·km-1 时，车辆换道频率呈“上凸”状，CAV渗透率越高，HDV凸形峰值越大，而CAV峰值较低；在高密度下，受可换道空间的约束，不同类型车辆均无法完成换道。此外，进一步讨论了不同CAV渗透率及密度条件下的异质交通流仿真效益，包括交通量提升及秩序改善特征等。研究成果有助于理解智能网联环境下多车道异质交通流运行状况，为未来异质交通流管理提供理论参考。

关键词: 交通工程, 异质交通流, 通行能力, 换道模型, 智能网联

CLC Number:

U491.2

SHAN Xiao-nian, WAN Chang-xin, LI Zhi-bin, ZHANG Xiao-li, CAO Chang-heng. 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.

单肖年, 万长薪, 李志斌, 张小丽, 曹昌衡. 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.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2022.06.008

http://www.tseit.org.cn/EN/Y2022/V22/I6/74

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Modeling and Simulation of Multi-lane Heterogeneous Traffic Flow in Intelligent and Connected Vehicle Environment

智能网联环境下多车道异质交通流建模与仿真

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