吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (5): 1285-1292.doi: 10.13229/j.cnki.jdxbgxb.20220770

• 交通运输工程·土木工程 • 上一篇    

考虑多车影响的智能网联车跟驰模型

蒲云1,2,3(),徐银1,2,3,刘海旭1,2,3(),谭一帆1,2,3,4   

  1. 1.西南交通大学 交通运输与物流学院，成都 610031
    2.西南交通大学 综合交通大数据应用技术国家工程实验室，成都 611756
    3.西南交通大学 综合交通运输智能化国家地方联合工程实验室，成都 610031
    4.威斯康星大学 麦迪逊分校 土木与环境工程系，威斯康星州 麦迪逊 53706
  • 收稿日期:2022-06-20 出版日期:2024-05-01 发布日期:2024-06-11
  • 通讯作者: 刘海旭 E-mail:ypu@home.swjtu.edu.cn;hxliu@swjtu.edu.cn
  • 作者简介:蒲云（1962-），男，教授，博士.研究方向：交通流理论，智能交通系统.E-mail：ypu@home.swjtu.edu.cn
  • 基金资助:
    湖北省交通运输厅科技项目(2022-11-1-5);国家自然科学基金项目(52002339);四川省科技计划项目(2021YJ0535)

An improved car⁃following model for connected and automated vehicles considering impact of multiple vehicles

Yun PU1,2,3(),Yin XU1,2,3,Hai-xu LIU1,2,3(),Yi-fan TAN1,2,3,4   

  1. 1.School of Transportation and Logistics，Southwest Jiaotong University，Chengdu 610031，China
    2.National Engineering Laboratory of Application Technology of Integrated Transportation Big Data SWJTU，Southwest Jiaotong University，Chengdu 611756，China
    3.National United Engineering Laboratory of Integrated and Intelligent Transportation，Southwest Jiaotong University，Chengdu 610031，China
    4.Department of Civil and Environment Engineering，University of Wisconsin-Madison，Madison 53706，USA
  • Received:2022-06-20 Online:2024-05-01 Published:2024-06-11
  • Contact: Hai-xu LIU E-mail:ypu@home.swjtu.edu.cn;hxliu@swjtu.edu.cn

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摘要：

为研究智能网联车（CAV）对交通流的影响，基于智能驾驶人模型（IDM），考虑后车和多前车速度差的影响，构建CAV智能驾驶跟驰模型，推导了CAV跟驰模型的临界稳定性条件。然后以考虑1辆后车和5辆前车速度差的CAV跟驰模型设计数值仿真实验。结果表明：交通流稳定性随考虑后车影响的权重比例变化而变化，即只有当后视权重比例在恰当范围内时才可提高交通流稳定性；考虑后车影响和多前车速度差还可削弱由时延导致的交通流不稳定性。同时，新模型控制下的车辆加速度变化更平稳，更有助于提高交通流稳定性和交通流安全性。

关键词: 交通工程, 跟驰模型, 智能网联车, 交通流稳定性, 安全性评价

Abstract:

To study the impact of connected autonomous vehicle （CAV） on traffic flow， based on the intelligent driver model （IDM）， a car-following model for CAV is constructed by considering the rear vehicle and velocity difference of multiple front vehicles simultaneously. Then critical stability condition is deduced by applying the linear stability analysis theory. Taking a rear vehicle and five-head vehicles into consideration， the numerical simulation is performed. The results show that under the backward looking effect context only when the backward weight ratio belongs to an appropriate range then the traffic flow stability can be enhanced. Furthermore， accounting for both the rear vehicle and the velocity difference of multiple preceding vehicles can also reduce the instability of traffic flow caused by time delay. The acceleration of the vehicle under the new model is gentler and more conducive to improve the stability and safety of traffic flow.

Key words: traffic engineering, car-following model, connected autonomous vehicle, traffic flow stability, safety evaluation

中图分类号: 

  • U491.1

图1

参数P不同取值时的临界稳定性曲线"

表1

模型参数取值"

参数取值参数取值
a/（m·s-2）1l/m6
b/（m·s-2）-2T/s1.1
vf /（m·s-1）30s0 /s2

图2

CAV加速场景仿真中的加速度、速度变化"

表2

不同CAV渗透率对TET的影响"

CAV渗透率不同do（s）取值的TET值降低百分比均值/%
2.02.53.03.5
00.000.000.000.00
10-19.18-14.45-9.42-4.77
20-57.84-37.13-35.16-24.62
30-67.36-63.30-52.29-50.99
40-78.48-72.31-69.29-60.43
50-78.93-75.52-71.61-63.60
60-83.85-77.50-75.38-71.67
70-86.28-81.32-76.40-74.08
80-89.81-84.32-81.46-78.70
90-90.07-86.41-84.03-80.39
100-91.11-87.51-84.34-81.07

表3

不同CAV渗透率对TIT的影响"

CAV渗透率不同do（s）取值的TIT值降低百分比均值/%
2.02.53.03.5
00.000.000.000.00
10-21.78-16.25-13.88-6.55
20-60.67-40.97-39.95-31.05
30-69.56-67.57-58.23-58.20
40-80.41-76.65-76.18-68.27
50-80.70-79.32-76.38-71.52
60-85.65-81.12-79.70-77.89
70-88.42-84.48-80.57-79.71
80-91.55-86.71-85.22-84.10
90-91.76-88.97-87.53-85.47
100-92.13-89.93-87.91-85.94
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