吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (6): 2015-2029.doi: 10.13229/j.cnki.jdxbgxb.20230987

• 交通运输工程·土木工程 • 上一篇    下一篇

基于密度离散度和信息传输延迟的网联商用车弯道格子模型

赵红专1,2,3,4,5(),吴泽健1,2,4,5,张鑫1,2,石胜文4,李文勇1,展新4,许恩永4,王佳明4,6   

  1. 1.桂林电子科技大学 广西智慧交通重点实验室,广西 桂林 541004
    2.桂林电子科技大学 建筑与交通工程学院,广西 桂林 541004
    3.桂林电子科技大学 广西精密导航技术与应用重点实验室,广西 桂林 541004
    4.东风柳州汽车有限公司,广西 柳州 545005
    5.南宁桂电电子科技研究院有限公司,南宁 530032
    6.广西科技大学 人文艺术与设计学院,广西 柳州 545006
  • 收稿日期:2023-09-14 出版日期:2025-06-01 发布日期:2025-07-23
  • 作者简介:赵红专(1985-),男,副教授,博士.研究方向:智慧交通.E-mail:zhaohongzhuan@guet.edu.cn
  • 基金资助:
    国家自然科学基金项目(52362045);广西重点研发计划项目(桂科AB21220052);广西科技重大专项项目(桂科AA22068101);柳州市重大专项项目(2021CAA0101);桂林市创新平台和人才计划项目(20210217-15);广西精密导航技术与应用重点实验室项目(DH202225)

Curve lattice model for connected commercial vehicles based on density dispersion and information transmission delay

Hong-zhuan ZHAO1,2,3,4,5(),Ze-jian WU1,2,4,5,Xin ZHANG1,2,Sheng-wen SHI4,Wen-yong LI1,Xin ZHAN4,En-yong XU4,Jia-ming WANG4,6   

  1. 1.Guangxi Key Laboratory of ITS,Guilin University of Electronic Technology,Guilin 541004,China
    2.School of Architecture and Transportation Engineering,Guilin University of Electronic Technology,Guilin 541004,China
    3.Guangxi Key Laboratory of Precision Navigation Technology and Application,Guilin University of Electronic Technology,Guilin 541004,China
    4.Dongfeng Liuzhou Automobile Co. ,Ltd. ,Liuzhou 545005,China
    5.GUET-Nanning E-Tech Research Institute Co. ,Ltd,Nanning 530032,China
    6.College of Humanities,Art and Design,Guangxi University of Science and Technology,Liuzhou 545006,China
  • Received:2023-09-14 Online:2025-06-01 Published:2025-07-23

摘要:

为缓解弯道环境下含有网联商用车的混合交通流交通拥塞问题,在安全条件的约束下提高弯道环境下含有网联商用车的混合交通流的稳定性,提出了一种弯道环境下基于密度离散度效应和信息传输延迟效应的网联商用车格子模型(DDITD)。该模型首次提出了密度离散度效应的概念,揭示了网联商用车跟驰特性对混合车流密度分布的影响,并将密度离散度效应和信息传输延迟效应同时引入弯道格子模型中,拓展了格子模型的适用范围。首先,通过实车试验标定DDITD模型中的转弯修正系数,利用线性稳定性分析对DDITD模型进行分析,研究了弯道环境下密度离散度以及信息传输延迟对交通流稳定性的影响。其次,通过非线性稳定性分析,应用约化摄动方法,推导出描述交通密度波演化特性临界点附近的mKdV方程。最后,通过数值仿真对理论结果进行了验证,结果表明,密度离散度效应和信息传输延迟效应可以有效缓解弯道上的交通堵塞。本文研究成果为研究网联商用车跟驰特性和混合车流稳定性提供了新的方法,为弯道环境下的交通管理和控制提供了新的思路和依据。

关键词: 交通运输系统工程, 格子流体力学模型, 弯道, 网联商用车, 密度离散度效应, 信息传输延迟效应

Abstract:

In order to solve the traffic congestion problem of mixed traffic flow with connected commercial vehicles in the curve environment and improve the stability of mixed traffic flow with connected commercial vehicles in the curve environment, a lattice model of connected commercial vehicles based on density dispersion effect and information transmission delay effect (DDITD) in curve environment was proposed. The density dispersion effect was first proposed, which reveals the influence of the following characteristics of connected commercial vehicles on the density distribution of mixed traffic flow. The density dispersion effect and the information transmission delay effect were introduced into the curve lattice model at the same time, which expands the applicable scope of the lattice model. Firstly, the turn correction coefficient in DDITD model was calibrated through real vehicle test, and then the DDITD model was analyzed by linear stability analysis to study the influence of density dispersion and information transmission delay on the stability of traffic flow under the curve environment. Secondly, the reduced perturbation method was applied through nonlinear stability analysis. The mKdV (modified Korteweg de Vries) equation was derived to describe the critical point of traffic density wave evolution. Finally, the theoretical results were verified by numerical simulation, and the results show that the density dispersion effect and the information transmission delay effect can effectively alleviate the traffic jam on the curve. The research results provides a new method for studying the following characteristics of connected commercial vehicles and the stability of mixed traffic flow, and provides a new ideas and basis for traffic management and control in curved environments.

Key words: engineering of communications and transportation system, lattice hydrodynamic model, curve, commercial vehicles, density dispersion effect, delayed effects of information transmission

中图分类号: 

  • U491.1

图1

弯道环境下网联商用车行驶路线物理模型"

图2

网联商用车车型参数标定流程"

表1

实验场景设置"

驾驶场景驾驶行为
右转弯启动加速行驶
高速匀速行驶
制动减速行驶
左转弯启动加速行驶
高速匀速行驶
制动减速行驶

表2

ft值随实测最大速度变化"

参数最小值中位数最大值
理论vmax77.5177.5177.51
实测右转vmax64.5765.3166.07
frt0.830.840.85
实测左转vmax67.5368.2468.94
flt0.870.880.89

图3

网联商用车转弯最大速度箱线图"

图4

密度-敏感度相图"

图5

临界敏感度ac分别与参数β、λ以及n的变化关系"

图6

t=1 551 000时步后的局域密度时空演化图"

图7

t=1 551 000时步后的局域密度时空演化图"

图8

t=1 551 000时步后的局域密度时空演化图"

图9

t=1 551 000时步的密度剖面图"

图10

t=1 551 000时步的密度剖面图"

图11

t=1 551 000时步的密度剖面图"

图12

不同n、β、λ值下的迟滞环"

[1] 陈龙, 刘孟协, 蔡英凤, 等.车路协同环境下考虑坡度与前车信息的跟驰模型[J].东南大学学报: 自然科学版, 2022, 52(4): 787-795.
Chen Long, Liu Meng-xie, Cai Ying-feng, et al. Car-following model considering road gradient and preceding vehicle information in vehicle-infrastructure cooperation environment[J]. Journal of Southeast University (Natural Science Edition), 2022, 52(4):787-795.
[2] 谢济铭, 彭博, 秦雅琴. 基于换道概率分布的多车道交织区元胞自动机模型[J]. 交通运输系统工程与信息, 2022, 22(3): 276-285.
Xie Ji-ming, Peng Bo, Qin Ya-qin. Cellular automata model of multi-lane weaving area based on lane-changing probability distribution[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 276-285.
[3] Zhang H M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model[J]. Transportation Research Part B, 2003, 37(1): 27-41.
[4] Nagatani T. Jamming transition of high-dimensional traffic dynamics[J]. Physica A: Statistical Mechanics and its Applications, 1999, 272(3/4): 592-611.
[5] Wang T, Gao Z, Zhang J, et al. A new lattice hydrodynamic model for two-lane traffic with the consideration of density difference effect[J]. Nonlinear Dynamics, 2014, 75(1): 27-34.
[6] Nagatani T. Modified KdV equation for jamming transition in the continuum models of traffic[J]. Physica A: Statistical Mechanics and its Applications, 1998, 261(3/4): 599-607.
[7] Ge H X, Cheng R J. The "backward looking" effect in the lattice hydrodynamic model[J]. Physica A: Statistical Mechanics and its Applications, 2008, 387(28): 6952-6958.
[8] Peng G H, Cai X H, Cao B F, et al. Non-lane-based lattice hydrodynamic model of traffic flow considering the lateral effects of the lane width[J]. Physics Letters A, 2011, 375(30/31): 2823-2827.
[9] Wang T, Gao Z Y, Zhao X M, et al. Flow difference effect in the two-lane lattice hydrodynamic model[J]. Chinese Physics B, 2012, 21(7): No.070507.
[10] Gupta A K, Redhu P. Analysis of a modified two-lane lattice model by considering the density difference effect[J]. Communications in Nonlinear Science and Numerical Simulation, 2014, 19(5): 1600-1610.
[11] Peng G. A new lattice model of two-lane traffic flow with the consideration of optimal current difference[J]. Communications in Nonlinear Science and Numerical Simulation, 2013, 18(3): 559-566.
[12] Nagatani T. Jamming transitions and the modified Korteweg-de Vries equation in a two-lane traffic flow[J]. Physica A: Statistical Mechanics and its Applications, 1999, 265(1/2): 297-310.
[13] Nagatani T. Jamming transition in a two-dimensional traffic flow model[J]. Physical Review E, 1999, 59(5): No.4857.
[14] Nagatani T. TDGL and MKdV equations for jamming transition in the lattice models of traffic[J]. Physica A: Statistical Mechanics and its Applications, 1999, 264(3/4): 581-592.
[15] Cao J L, Shi Z K. A novel lattice traffic flow model on a curved road[J]. International Journal of Modern Physics C, 2015, 26(11): No.1550121.
[16] Kaur R, Sharma S. Analysis of driver's characteristics on a curved road in a lattice model[J]. Physica A: Statistical Mechanics and its Applications, 2017, 471: 59-67.
[17] Kaur R, Sharma S. Modeling and simulation of driver's anticipation effect in a two lane system on curved road with slope[J]. Physica A: Statistical Mechanics and its Applications, 2018, 499: 110-120.
[18] Cheng R, Wang Y. An extended lattice hydrodynamic model considering the delayed feedback control on a curved road[J]. Physica A: Statistical Mechanics and its Applications, 2019, 513: 510-517.
[19] Wang Z, Zhu W X. Modeling and stability analysis of traffic flow considering electronic throttle dynamics on a curved road with slope[J]. Physica A: Statistical Mechanics and its Applications, 2022, 597: No.127225.
[20] Redhu P, Gupta A K. Delayed-feedback control in a Lattice hydrodynamic model[J]. Communications in Nonlinear Science & Numerical Simulation, 2015, 27(1/3):263-270.
[21] Peng G, Yang S, Zhao H. A delayed-feedback control method for the lattice hydrodynamic model caused by the historic density difference effect[J]. Physica A: Statistical Mechanics and its Applications, 2018, 509:855-860.
[22] Peng G, Yang S, Xia D, et al. Delayed-feedback control in a car-following model with the combination of V2V communication[J]. Physica A: Statistical Mechanics and its Applications, 2019, 526: No.120912.
[23] Zhang Y, Wang S, Pan D, et al. Stability analysis for a new lattice hydrodynamic model with time-varying delay in sensing traffic flux[J]. Physica A: Statistical Mechanics and its Applications, 2021, 561: No.125269.
[24] 罗春莉. V2X环境下基于交通信息协同传输的优化建模与能耗控制[D]. 桂林:广西师范大学物理科学与技术学院, 2023.
Luo Chun-li. Optimization modeling and energy consumption control based on traffic information collaborative transmission under V2X environment[D]. Guilin:College of Physical Science and Technology, Guangxi Normal University, 2023.
[25] 贾特提. V2X环境下基于协同信息传输延迟的交通系统优化建模与能耗控制[D]. 桂林:广西师范大学物理科学与技术学院, 2023.
Jia Te-ti. Optimal modeling and energy consumption control of traffic system based on cooperative information transmission delay under V2X environment[D]. Guilin:College of Physical Science and Technology, Guangxi Normal University, 2023.
[26] 贾洪飞, 隽志才, 魏丽英. 饱和流率的部分影响因素修正系数的标定[J]. 吉林大学学报: 工学版, 2000, 30(2): 99-102.
Jia Hong-fei, Zhi-cai Juan, Wei Li-ying. Calibration to the revision coefficients of some factors affecting saturation flow rate[J]. Journal of Jilin University (Engineering and Technology Edition), 2000,30(2): 99-102.
[27] 王宪彬, 施树明, 裴玉龙. 面向网联商用车行驶工况优化设计的高速公路工况识别[J]. 中国公路学报, 2022, 35(6): 355-362.
Wang Xian-bin, Shi Shu-ming, Pei Yu-long. Identification of expressway driving cycles for optimization of commercial vehicle driving cycles[J]. China Journal of Highway and Transport, 2022, 35(6): 355-362.
[28] 唐易, 刘诗昆, 刘恒. 基于多源数据的高速公路交通运行特征与治堵策略研究:以深圳市机荷高速为例[J]. 交通与运输, 2022, 35(S1): 132-138.
Tang Yi, Liu Shi-kun, Liu Heng. Research on traffic operation characteristics of expressway and control strategy of traffic congestion based on multi-source data:Taking Shenzhen Jihe Expressway as an example[J]. Traffic & Transportation, 2022, 35(Sup.1): 132-138.
[29] Zhou J, Shi Z K. Lattice hydrodynamic model for traffic flow on curved road[J]. Nonlinear Dynamics, 2016, 83: 1217-1236.
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