[1]安树科,徐良杰,钱良辉,等.考虑前方多车优化速度信息的车辆跟驰模型[J].东南大学学报(自然科学版),2020,50(6):1156-1162.[doi:10.3969/j.issn.1001-0505.2020.06.024]
　An Shuke,Xu Liangjie,Qian Lianghui,et al.Car-following model with optimal velocity information of multiple-vehicle ahead[J].Journal of Southeast University (Natural Science Edition),2020,50(6):1156-1162.[doi:10.3969/j.issn.1001-0505.2020.06.024]
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考虑前方多车优化速度信息的车辆跟驰模型()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
50
期数:
2020年第6期
页码:
1156-1162
栏目:
经济与管理
出版日期:
2020-11-20

文章信息/Info

Title:
Car-following model with optimal velocity information of multiple-vehicle ahead
作者:
安树科1; 徐良杰1; 2; 钱良辉3; 陈国俊1
1武汉理工大学交通学院, 武汉 430070; 2湖北文理学院汽车与交通工程学院, 襄阳 435003; 3西南林业大学继续教育学院, 昆明 650224
Author(s):
An Shuke1;  Xu Liangjie1; 2;  Qian Lianghui3;  Chen Guojun1
1School of Transportation, Wuhan University of Technology, Wuhan 430070, China
2School of Automotive and Traffic Engineering, Hubei University of Arts and Science, Xiangyang 435003, China
3School of Continuing Education, Southwest Forestry University, Kunming 650224, China
关键词:
车路协同;  优化速度;  车辆跟驰模型;  稳定性分析
Keywords:
vehicle-infrastructure cooperation;  optimal velocity;  car-following model;  stability analysis
分类号:
F560
DOI:
10.3969/j.issn.1001-0505.2020.06.024
摘要:
基于车路协同技术完全信息可达性的特点,引入车头间距反馈信息,并提出一种考虑前方多辆车优化速度信息的改进车辆跟驰模型,同时加入驾驶员在不同车头间距条件下的感知特性,改进了多速度差反馈控制策略.基于Lyapunov方法推导了改进模型的线性稳定性,并获得其稳定性条件.通过数值仿真方法分析了多前车优化速度和速度差信息对交通流稳定性的影响.研究结果表明:较已有MVD模型,考虑前方多辆车优化速度使得扰动作用时间减少了约30.3%;在频繁扰动作用下,引入多前车优化速度信息更有利于交通流稳定,且当参数N增至3时,尾车的停滞时间减少了74.0%;当通信车辆数不变时,通过拓展跟驰车辆的信息维度,可以提高交通流的稳定性,抑制交通拥堵形成.
Abstract:
Based on the characteristics of complete information accessibility of vehicle-infrastructure cooperation technology, the feedback information of headway was introduced, and an improved car-following model with the optimal velocity information of multiple-vehicle ahead(OVIMA)was proposed. Meanwhile, the drivers’ perception characteristics under different headway conditions were added to improve the multi-speed difference feedback control strategy. The linear stability of the improved model was derived based on the Lyapunov method and the stability conditions were obtained. Then, the numerical simulation method was used to analyze the influences of OVIMA and velocity difference information of multiple-vehicle ahead(VDIMA)on the traffic flow stability. The results show that, compared with existing MVD models, the acting time of the disturbance can be reduced by about 30.3% by considering the optimal velocity of multiple-vehicle ahead. In the case of frequent disturbances, the introduction of OVIMA is more conducive to the stability of the traffic flow. When the parameter N is increased to 3, the stagnation time of the chaser is reduced by 74.0%. The stability of the traffic flow can be further improved and the traffic congestion can be suppressed by expanding the information dimension of vehicles on the same number of communication vehicles.

参考文献/References:

[1] Zhou J, Shi Z K. A new lattice hydrodynamic model for bidirectional pedestrian flow with the consideration of pedestrian’s anticipation effect[J].Nonlinear Dynamics, 2015, 81(3): 1247-1262. DOI: 10. 1007/s11071 – 015 – 2065-6.
[2] Cheng R J, Ge H X, Wang J F. An extended continuum model accounting for the driver’s timid and aggressive attributions[J].Physics Letters A, 2017, 381(15): 1302-1312. DOI: 10. 1016/j. physleta. 2017. 02. 018.
[3] Tang T Q, Rui Y X, Zhang J, et al. A cellular automation model accounting for bicycle’s group behavior[J].Physica A, 2018, 492: 1782-1797. DOI: 10. 1016/j. physa. 2017. 11. 097.
[4] Treiber M, Kesting A. The intelligent driver model with stochasticity-new insights into traffic flow oscillations[J]. Transportation Research Procedia, 2017, 23: 174-187. DOI: 10. 1016/j. trpro. 2017. 05. 011.
[5] Newell G F. Nonlinear effects in the dynamics of car following[J].Operations Research, 1961, 9(2): 209-229. DOI: 10. 1287/opre. 9. 2. 209.
[6] Bando M,Hasebe K, Nakayama A, et al. Dynamical model of traffic congestion and numerical simulation[J]. Physical Review E, 1995, 51(2): 1035-1042. DOI: 10. 1103/physreve. 51. 1035.
[7] Helbing D,Tilch B. Generalized force model of traffic dynamics[J]. Physical Review E, 1998, 58(1): 133-138. DOI: 10. 1103/PhysRevE. 58. 133.
[8] Jiang R, Wu Q S, Zhu Z J. Full velocity difference model for a car-following theory[J].Physical Review E, 2001, 64: 017101. DOI: 10. 1103/physreve. 64. 017101.
[9] 安树科, 徐良杰, 陈国俊, 等. 基于车路协同技术的信号交叉口改进车辆跟驰模型[J]. 东南大学学报(自然科学版), 2020, 50(1): 169-174. DOI: 10. 3969/j. issn. 1001-0505. 2020. 01. 022.
An S K, Xu L J, Chen G J, et al. Improved car-following model at signalized intersection based on vehicle-infrastructure cooperation technology[J].Journal of Southeast University(Natural Science Edition), 2020, 50(1): 169-174. DOI:10. 3969/j. issn. 1001-0505. 2020. 01. 022. (in Chinese)
[10] 翟聪, 刘伟铭, 谭飞刚. 一类耦合映射模糊时滞跟驰系统的反馈控制[J]. 华南理工大学学报(自然科学版), 2017, 45(1): 9-17. DOI: 10. 3969/j. issn. 1000-565X. 2017. 01. 002.
Zhai C, Liu W M, Tan F G. Feedback control of a class of coupled-map fuzzy time-delay car-following system[J]. Journal of South China University of Technology(Natural Science Edition), 2017, 45(1): 9-17. DOI:10. 3969/j. issn. 1000-565X. 2017. 01. 002. (in Chinese)
[11] Guo Y,Xue Y, Shi Y, et al. Mean-field velocity difference model considering the average effect of multi-vehicle interaction[J]. Communications in Nonlinear Science and Numerical Simulation, 2018, 59: 553-564. DOI: 10. 1016/j. cnsns. 2017. 11. 026.
[12] An S K, Xu L J, Chen G J, et al. A new car-following model on complex road considering driver’s characteristics[J].Modern Physics Letters B, 2020, 34(16): 2050182. DOI: 10. 1142/s0217984920501821.
[13] Zhu W X, Zhang H M. Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model[J].Physica A, 2018, 496: 274-285. DOI: 10. 1016/j. physa. 2017. 12. 103.
[14] Ge H X, Zhu H B, Dai S Q. Effect of looking backward on traffic flow in a cooperative driving car following model[J].The European Physical Journal B, 2006, 54(4): 503-507. DOI: 10. 1140/epjb/e2007-00014-x.
[15] Yu L, Shi Z K, Zhou B C. Kink-antikink density wave of an extended car-following model in a cooperative driving system[J].Communications in Nonlinear Science and Numerical Simulation, 2008, 13(10): 2167-2176. DOI: 10. 1016/j. cnsns. 2007. 07. 008.
[16] 周桐, 郑林江, 刘卫宁, 等. 考虑多车稳态期望速度效应的拥堵控制跟驰模型[J]. 重庆大学学报, 2018, 41(1): 51-60. DOI: 10. 11835/j. issn. 1000-582X. 2018. 01. 006.
Zhou T, Zheng L J, Liu W N, et al. A new congestion control car-following model with consideration of multi-vehicles’ steady-state desired speed effect[J].Journal of Chongqing University, 2018, 41(1): 51-60. DOI:10. 11835/j. issn. 1000-582X. 2018. 01. 006. (in Chinese)
[17] Jiang R, Hu M B, Zhang H M, et al. On some experimental features of car-following behavior and how to model them[J].Transportation Research Part B, 2015, 80: 338-354. DOI: 10. 1016/j. trb. 2015. 08. 003.
[18] 韩祥临, 姜长元, 葛红霞, 等. 基于智能交通系统的耦合映射跟驰模型和交通拥堵控制[J]. 物理学报, 2007, 56(8): 4383-4392. DOI: 10. 3321/j. issn: 1000-3290. 2007. 08. 013.
Han X L, Jiang C Y, Ge H X, et al. A modified coupled map car-following model based on application of intelligent transportation system and control of traffic congestion[J].Acta Physica Sinica, 2007, 56(8): 4383-4392. DOI:10. 3321/j. issn: 1000-3290. 2007. 08. 013. (in Chinese)
[19] 王祺, 李力, 胡坚明, 等. 不同车头间距下交通流的速度分布[J]. 清华大学学报(自然科学版), 2011, 51(3): 309-312. DOI: 10. 16511/j. cnki. qhdxxb. 2011. 03. 020.
Wang Q, Li L, Hu J M, et al. Traffic velocity distributions for different spacings[J].Journal of Tsinghua University(Science and Technology), 2011, 51(3): 309-312. DOI:10. 16511/j. cnki. qhdxxb. 2011. 03. 020. (in Chinese)
[20] 王涛, 高自友, 赵小梅. 多速度差模型及稳定性分析[J]. 物理学报, 2006, 55(2): 634-640. DOI: 10. 3321/j. issn: 1000-3290. 2006. 02. 028.
Wang T, Gao Z Y, Zhao X M. Multiple velocity difference model and its stability analysis[J]. Acta Physica Sinica, 2006, 55(2): 634-640. DOI:10. 3321/j. issn: 1000-3290. 2006. 02. 028. (in Chinese)
[21] Zheng Y M, Cheng R J, Ge H X. The feedback control research on straight and curved road with car-following model[J].Physics Letters A, 2017, 381(27): 2137-2143. DOI: 10. 1016/j. physleta. 2017. 04. 041.
[22] Zhu W X, Zhang H M. Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model[J].Physica A, 2018, 496: 274-285. DOI: 10. 1016/j. physa. 2017. 12. 103.

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备注/Memo

备注/Memo:
收稿日期: 2020-05-06.
作者简介: 安树科(1992—),男,博士生;徐良杰(联系人),女,博士,教授,博士生导师,laurrie119@163.com.
基金项目: 国家自然科学基金资助项目(51578433).
引用本文: 安树科,徐良杰,钱良辉,等.考虑前方多车优化速度信息的车辆跟驰模型[J].东南大学学报(自然科学版),2020,50(6):1156-1162. DOI:10.3969/j.issn.1001-0505.2020.06.024.
更新日期/Last Update: 2020-11-20