|Table of Contents|

Configuration method of autonomous driving vehicles dedicated lane at urban road intersections in intelligent vehicle-road environment(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2026年01期
Page:
175-188
Research Field:
交通工程
Publishing date:

Info

Title:
Configuration method of autonomous driving vehicles dedicated lane at urban road intersections in intelligent vehicle-road environment
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)
Keywords:
intelligent transportation;  autonomous driving;  lane configuration;  cross intersection;  urban road;  CAV
PACS:
U491.4
DOI:
10.19721/j.cnki.1671-8879.2026.01.013
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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Last Update: 2026-02-20