[1]王小霞,潘智成,黄琛*,等.智能车路环境下城市道路交叉口自动驾驶车辆专用道配置方法[J].长安大学学报(自然科学版),2026,46(01):175-188.[doi:10.19721/j.cnki.1671-8879.2026.01.013]
　WANG Xiao-xia,PAN Zhi-cheng,HUANG Chen*,et al.Configuration method of autonomous driving vehicles dedicated lane at urban road intersections in intelligent vehicle-road environment[J].Journal of Chang’an University (Natural Science Edition),2026,46(01):175-188.[doi:10.19721/j.cnki.1671-8879.2026.01.013]
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智能车路环境下城市道路交叉口自动驾驶车辆专用道配置方法()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第46卷
期数:
2026年01期
页码:
175-188
栏目:
交通工程
出版日期:
2026-01-31

文章信息/Info

Title:
Configuration method of autonomous driving vehicles dedicated lane at urban road intersections in intelligent vehicle-road environment
文章编号:
1671-8879(2026)01-0175-14
作者:
王小霞1; 潘智成1; 黄琛2*; 黄斐超2
(1. 广东工业大学 土木与交通工程学院,广东 广州 510006; 2. 广东工业大学 总务部,广东 广州 510006)
Author(s):
WANG Xiao-xia1;  PAN Zhi-cheng1;  HUANG Chen2*;  HUANG Fei-chao2
(1. School of Civil and Transportation Engineering, Guangdong University of Technology,Guangzhou 510006, Guangdong, China; 2. General Affairs Department, Guangdong University of Technology, Guangzhou 510006, Guangdong, China)
关键词:
智能交通;  自动驾驶;  车道配置;  十字交叉口;  城市道路;  网联自动驾驶车辆
Keywords:
intelligent transportation;  autonomous driving;  lane configuration;  cross intersection;  urban road;  CAV
分类号:
U491.4
DOI:
10.19721/j.cnki.1671-8879.2026.01.013
文献标志码:
A
摘要:
利用Krauss模型和智能驾驶模型(IDM)分别描述了网联辅助驾驶车辆(CHV)和网联自动驾驶车辆(CAV)的跟驰行为和行驶特征,考虑交叉口进口道的交通需求与CAV渗透率的变化,以进口道中不同功能车道交通流量分配均衡为约束,使用绿灯时间利用率和转变指标建立了CAV专用车道配置方法; 结合直行待行区设计了1个直行CAV专用车道动态配置(方案1)与2个直行CAV专用车道动态配置(方案2),提出了对应的CAV专用车道配置管理方法; 采用随机抽样法确定了仿真试验的交通流量输入参数,利用SUMO和Python搭建了交通仿真模型,对比分析了CAV专用车道方案的实施效果; 采用敏感性分析方法,从交通流量和CAV渗透率的角度分析了CAV专用车道方案的适用情景。研究结果表明:CAV渗透率为50%的情况下,不同车道配置方案对交叉口车均延误和停车次数的影响差异显著,相较于原交通管控方案,方案1可使车均延误减少5.79%,能有效减轻车辆混合通行造成的交通拥堵,充分发挥CAV的跟驰性能,提高道路通行效率; CAV渗透率小于35%时不宜设置CAV专用车道,CAV渗透率处于35%～70%时1个直行CAV专用车道的通行效率最高; CAV渗透率大于70%时2个直行CAV专用车道的车均延误最低; 在车流饱和状态下,CAV专用车道方案下的车均延误相较于原交通管控方案下降了14%。
Abstract:
The Krauss model and intelligent driver model(IDM)were utilized to describe the car-following behavior and driving characteristics of connected human-driven vehicle(CHV)and connected and automated vehicle(CAV), respectively. Considering the variations in traffic demand and CAV penetration rate at intersection approaches, a configuration method for CAV dedicated lanes was established using the green time utilization and transition indicators, constrained by the equilibrium of traffic flow distributions among different functional lanes. In conjunction with the design of straight-through waiting area, two dynamic configuration schemes for one(scheme Ⅰ)and two(scheme Ⅱ)straight CAV dedicated lanes were developed, along with their corresponding management methods for CAV dedicated lane configuration. The traffic flow input parameters for the simulation trials were determined using a random sampling method. A traffic simulation model was constructed using the SUMO and Python to comparatively analyze the implementation effects of CAV dedicated lane schemes. The sensitivity analysis was employed to investigate the applicable scenarios of these CAV dedicated lane schemes from the perspectives of traffic flow and CAV penetration rate. The research results indicate that at a CAV penetration rate of 50%, different lane configuration schemes exert significant impacts on the average vehicle delay and number of stops at the intersection. Compared to the original traffic control scheme, scheme Ⅰ can reduce the average vehicle delay by 5.79%, effectively mitigating the congestion caused by mixed traffic and fully leverage the car-following performance of CAV, and improving the road traffic efficiency. When the CAV penetration rate is less than 35%, it is not advisable to set up CAV dedicated lanes. When the CAV penetration rate is 35%-70%, the traffic efficiency of one straight CAV dedicated lane is the highest. When the CAV penetration rate exceeds 70%, the average vehicle delay reaches its minimum under two straight CAV dedicated lanes scheme. In a state of saturated traffic flow, the average vehicle delay under CAV dedicated lane schemes decreases by 14% compared to the original traffic control scheme.7 tabs, 11 figs, 36 refs.

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备注/Memo

备注/Memo:
收稿日期:2025-06-28
基金项目:国家自然科学基金项目(62273102,42571352)
作者简介:王小霞(1981-),女,山东潍坊人,副教授,工学博士,从事智慧交通运输系统规划、大数据分析及应用研究,E-mail:wxx@gdut.edu.cn。
通信作者:黄 琛(1973-),男,河北赤城人,高级工程师,E-mail:hchen@gdut.edu.cn。
更新日期/Last Update: 2026-02-20