Car-following model of intelligent connected vehicles based on time-delayed velocity difference and velocity limit

doi:10.11772/j.issn.1001-9081.2021081425

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (9): 2936-2942.DOI: 10.11772/j.issn.1001-9081.2021081425

• Frontier and comprehensive applications • Previous Articles Next Articles

Car-following model of intelligent connected vehicles based on time-delayed velocity difference and velocity limit

Kaiwang ZHANG¹(), Fei HUI¹, Guoxiang ZHANG², Qi SHI¹, Zhizhong LIU²

^1.School of Information Engineering，Chang’an University，Xi’an Shaanxi 710064，China
^2.Yanchong Preparation Office，Hebei Expressway Group Limited，Zhangjiakou Hebei 075400，China

Received:2021-08-09 Revised:2021-12-02 Accepted:2021-12-02 Online:2022-01-07 Published:2022-09-10
Contact: Kaiwang ZHANG
About author:HUI Fei， born in 1982， Ph. D.， professor. His research interests include IoV.
ZHANG Guoxiang， born in 1979. His research interests include expressway construction.
SHI Qi， born in 1998， M. S. candidate. His research interests include IoV.
LIU Zhizhong， born in 1964. His research interests include expressway construction.
Supported by:
National Key Research and Development Program of China(2018YFB1600604);Science and Technology Program of Hebei Province(20470801D)

考虑时延速度差和限速信息的智能网联车跟驰模型

张凯望¹(), 惠飞¹, 张国祥², 石琦¹, 刘志忠²

^1.长安大学信息工程学院，西安 710064
^2.河北省高速公路集团有限公司延崇筹建处，河北张家口 075400

通讯作者: 张凯望
作者简介:惠飞（1982—），男，安徽濉溪人，教授，博士，CCF会员，主要研究方向：IoV；
张国祥（1979—），男，河北石家庄人，主要研究方向：高速公路建设；
石琦（1998—），男，陕西商洛人，硕士研究生，主要研究方向：IoV；
刘志忠（1964—），男，河北石家庄人，主要研究方向：高速公路建设。
基金资助:
国家重点研发计划项目(2018YFB1600604);河北省省级科技计划项目(20470801D)

Abstract

Abstract:

Focusing on the problems of disturbed car-following behavior and instability of traffic flow caused by the uncertainty of the driver’s acquisition of road velocity limit and time delay information， a car-following model TD-VDVL （Time-Delayed Velocity Difference and Velocity limit） was proposed with the consideration of the time-delayed velocity difference and the velocity limit information in the Internet of Vehicles （IoV） environment. Firstly， the speed change caused by time delay and road velocity limit information were introduced to improve the Full Velocity Difference （FVD） model. Then， the linear spectrum wave perturbation method was used to derive the traffic flow stability judgment basis of TD-VDVL model， and the influence of each parameter in the model on the stability of the system was analyzed separately. Finally， the numerical simulation experiments and comparative analysis were carried out using Matlab. In the simulation experiments， straight roads and circular roads were selected， and slight disturbance was imposed on the fleet during driving. When conditions were the same， TD-VDVL model had the smallest velocity fluctuation rate and the fluctuation of fleet headway compared to the Optimal Velocity （OV） and FVD models. Especially when the sensitivity coefficient of the velocity limit information was 0.3， and the sensitivity coefficient of the time-delayed speed difference was 0.3， the proposed model had the average fluctuation rate of the fleet velocity reached 2.35% at time of 500 s， and the peak and valley difference of fleet headway of only 0.019 4 m. Experimental results show that TD-VDVL model has a better stable area after introducing time-delayed velocity difference and velocity limit information， and can significantly enhance the ability of car-following fleet to absorb disturbance.

Key words: car-following model, time-delayed velocity difference, velocity limit, stability analysis, numerical simulation

摘要：

针对由于驾驶员对于道路限速和时延信息获取的不确定性而引起的跟驰行为受扰和交通流失稳等问题，提出了一种车联网（IoV）环境下考虑时延速度差和限速信息的跟驰模型TD-VDVL。首先，引入时延导致的速度变化量和道路限速信息对全速差（FVD）模型进行改进；然后，利用线性谱波微扰法推导出TD-VDVL模型的交通流稳定性判断依据，并分析模型中各参数对系统稳定性的影响；最后，利用Matlab进行数值仿真实验与对比分析。仿真实验中，分别选取在笔直道路和环形道路，给行驶过程中的车队施加轻微扰动。当条件一致时，TD-VDVL模型比优化速度（OV）、FVD模型中车队的速度波动率和车头间距起伏均小，尤其是当限速信息的敏感系数取0.3、时延速度差的敏感系数取0.3时，所提模型的车队速度平均波动率在时间500 s时可以达到2.35%，车头间距波峰波谷差仅为0.019 4 m。实验结果表明，TD-VDVL模型在引入时延速差和限速信息后，具备更优的稳定区域，能够明显增强跟驰车队吸收扰动的能力。

关键词: 跟驰模型, 时延速度差, 限速, 稳定性分析, 数值仿真

CLC Number:

U491

Kaiwang ZHANG, Fei HUI, Guoxiang ZHANG, Qi SHI, Zhizhong LIU. Car-following model of intelligent connected vehicles based on time-delayed velocity difference and velocity limit[J]. Journal of Computer Applications, 2022, 42(9): 2936-2942.

张凯望, 惠飞, 张国祥, 石琦, 刘志忠. 考虑时延速度差和限速信息的智能网联车跟驰模型[J]. 《计算机应用》唯一官方网站, 2022, 42(9): 2936-2942.

Figures/Tables 7

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模型	最小速度/（m·s^-1）	最大速度/（m·s^-1）	平均速度/（m·s^-1）	向上波动率/%	向下波动率/%	平均波动率/%
OV	0.053 6	1.946 9	1.137 5	71.15	95.28	83.21
FVD	0.319 8	1.705 6	1.061 1	60.74	69.85	65.29
TD-VDVL	1.258 8	1.321 7	1.280 9	3.19	1.71	2.45

模型	最小速度/（m·s^-1）	最大速度/（m·s^-1）	平均速度/（m·s^-1）	向上波动率/%	向下波动率/%	平均波动率/%
OV	0.053 6	1.946 9	1.137 5	71.15	95.28	83.21
FVD	0.319 8	1.705 6	1.061 1	60.74	69.85	65.29
TD-VDVL	1.258 8	1.321 7	1.280 9	3.19	1.71	2.45

模型	最小速度/（m·s^-1）	最大速度/（m·s^-1）	平均速度/（m·s^-1）	向上波动率/%	向下波动率/%	平均波动率/%
OV	0.050 2	1.952 7	1.206 3	61.86	95.83	78.84
FVD	0.265 6	1.753 6	1.100 7	59.32	75.86	67.59
TD-VDVL	1.283 4	1.344 2	1.314 1	2.29	2.34	2.35

模型	最小速度/（m·s^-1）	最大速度/（m·s^-1）	平均速度/（m·s^-1）	向上波动率/%	向下波动率/%	平均波动率/%
OV	0.050 2	1.952 7	1.206 3	61.86	95.83	78.84
FVD	0.265 6	1.753 6	1.100 7	59.32	75.86	67.59
TD-VDVL	1.283 4	1.344 2	1.314 1	2.29	2.34	2.35

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Car-following model of intelligent connected vehicles based on time-delayed velocity difference and velocity limit

考虑时延速度差和限速信息的智能网联车跟驰模型

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