考虑前后车辆综合信息的耦合映射跟驰模型

交通运输系统工程与信息 ›› 2009, Vol. 9 ›› Issue (2): 62-68 .

考虑前后车辆综合信息的耦合映射跟驰模型

韩祥临^*1,2;李兴莉³;姜长元²

1. 上海大学上海市应用数学和力学研究所，上海 200072; 2.湖州师范学院理学院，浙江湖州 313000; 3.太原科技大学应用科学学院，太原 030024

收稿日期:2008-09-19 修回日期:2009-01-13 出版日期:2009-04-25 发布日期:2009-04-25
通讯作者: 韩祥临
作者简介:韩祥临（1965-），男，山东沾化人，教授，博士生.
基金资助:
国家基础研究发展计划（2006CB705500），国家自然科学基金重点项目（10532060），国家自然科学基金（10471039,10602025），浙江省自然科学基金（Y606268）和太原科技大学博士科研启动项目（20082017）.

Modified Coupled Map Car-Following Model Based on Comprehensive Information of Preceding and Following Cars

HAN Xiang-Lin^1,2;LI Xing-li³; JIANG Chang-yuan²

1. Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai 200072, China; 2. College of Science, Huzhou University, Huzhou 313000, China; 3. College of Applied Science, Taiyuan University of Science and Technology, Taiyuan 030024, China

Received:2008-09-19 Revised:2009-01-13 Online:2009-04-25 Published:2009-04-25
Contact: HAN Xiang-Lin

摘要/Abstract

摘要： 基于智能交通诱导信息考虑前方多辆车，司机又通过后视镜考虑后方车辆，提出一种改进的耦合映射跟驰模型，用于描述单车道的交通流的动力学特性及其拥堵控制。利用离散延迟反馈控制动力学模式，通过调整模型中控制参数给出了抑制交通拥堵的策略。利用反馈控制理论，导出了在头车速度发生变化时，交通流保持稳定性的条件。分析结果表明，考虑前后方更多车辆的信息对交通流有致稳作用，亦即稳定性条件明显减弱。数值模拟证实了理论分析的正确性，通过与前人相关工作的比较得知，考虑前后方车辆的信息能够更有效地抑制交通拥堵。

关键词: 交通流, 智能交通系统, 耦合映射跟驰模型, 交通拥堵控制

Abstract: With the consideration of the application of intelligent transportation system (ITS) information, a modified coupled-map car-following model is proposed to describe the dynamical behavior of vehicles moving along a single-lane road. It is assumed that there exists a vehicle-bound navigation system providing each vehicle with the information about its preceding and following vehicles. Utilizing a dynamical version of the decentralized delayed-feedback control scheme, a strategy for controlling the traffic congestion is proposed through adjusting the control parameters in the presented model. Based on a new principle of feedback control, the stability criteria are provided as the speed of the preceding vehicle changes. The theoretical analysis shows that considering more information on preceding and following vehicles could keep traffic flows in stable conditions, which means that it is considerably easier to achieve the stable conditions of traffic flows. The corresponding numerical simulations confirm the correctness of the theoretical analysis. Compared with previous researches concerning congestion control, the control strategy in this paper is more effective in suppressing the formation of traffic congestion.

Key words: traffic flow, intelligent transportation system (ITS), coupled map car-following model, control of traffic congestion

中图分类号:

U491

韩祥临,李兴莉,姜长元. 考虑前后车辆综合信息的耦合映射跟驰模型[J]. 交通运输系统工程与信息, 2009, 9(2): 62-68 .

HAN Xiang-Lin,LI Xing-li,JIANG Chang-yuan. Modified Coupled Map Car-Following Model Based on Comprehensive Information of Preceding and Following Cars[J]. Journal of Transportation Systems Engineering and Information Technology, 2009, 9(2): 62-68 .

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract17787.shtml

http://www.tseit.org.cn/CN/Y2009/V9/I2/62

参考文献

[1] Hou Z S, Xu J X, Yan J W. An iterative leaning approach for density control of freeway traffic flow via ramp metering[J], Transportation Research Part C, 2008,15(1)71-97.

[2] 唐铁桥, 黄海军, 薛郁. 一种改进的两车道交通流格子模型[J]. 物理学报, 2006,55(8): 4026-4031. [Tang T Q, Huang H J, Xue Y. An improved two-lane traffic flow lattice model[J]. Acta Physica Sinica, 2006,55 (8): 4026-4031.]

[3] 黄乒花, 谭惠丽, 孔令江, 刘慕仁. 放边界条件下二维可转向主干道交通流模型的研究[J]. 物理学报, 2005,54(7), 3044-3050. [Huang P H, Tan H L, Kong L J, Liu M R. A study of a main-road traffic flow model with turning-probability under open-bound condition[J]. Acta Physica Sinica, 2005,54 (7), 3044-3050.]

[4]Nakayama A, Sugiyama Y,Hasebe K. Effect of looking at the car that follows in an optimal velocity model of traffic flow[J]. Physical Review E,2001,65(1),016112.

[5] Hasebe K, Nakayama A, Sugiyama Y. Dynamical model of a cooperative driving system for freeway traffic[J]. Physical Review E,2001,68(2),026102.

[6] Kerner B S, Konhauser P, Schilker M. Deterministic spontaneous appearance of traffic jams in slightly inhomogeneous traffic flow[J]. Physical Review E,1995,51(6),6243.

[7] Komatsu T S, Sasa S. Kink soliton characterizing traffic congestion[J]. Physical Review E,1995,52(5), 5574.

[8] Treiber M, Hennecke A, Helbing D. Congested traffic states in empirical observations and microscopic simulations[J]. Physical Review E,2000,62(2),1805-1824.

[9] Yukawa S, Kikuchi M. Coupled-Map modeling of one-dimensional traffic flow[J]. Journal of the Physical Society of Japan, 1995, 64, 35-38.

[10] Tadaki S, Kikuchi M, Sugiyama Y, Yukawa S. Coupled map traffic flow simulator based on optimal velocity functions[J]. Journal of the Physical Society of Japan, 1998, 67:2270-2276.

[11] Konishi K, Kokame H, Hirata K. Coupled map car-following model and its delayed-feedback control[J]. Physical Review E,1999,60(4):4000-4007.

[12] Zhao X M, Gao Z Y. A control method for congested traffic induced by bottlenecks in the coupled map car-following mode[J]. Physica A,2006,366, 513-522.

[13] 韩祥临, 姜长元, 葛红霞, 戴世强. 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. [Han X L, Jiang C Y, Ge H X, Dai S Q. 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.]

[14] Ge H X, Dai S D, Dong L Y, Xue Y. Stabilization effect of traffic flow in an extended car-following model based on an intelligent transportation system application[J]. Physical Review E,2004,70(6):066134.

[1]	陈小红, 蓝秋雨. 不确定交通需求下交叉口信号配时区间优化模型[J]. 交通运输系统工程与信息, 2022, 22(2): 247-256.
[2]	贺正冰, 徐瑞康, 谢东繁, 宗芳, 钟任新. 数据驱动跟驰模型综述[J]. 交通运输系统工程与信息, 2021, 21(5): 102-113.
[3]	龙建成, 郭嘉琪. 动态交通分配问题研究回顾与展望[J]. 交通运输系统工程与信息, 2021, 21(5): 125-138.
[4]	田钧方, 朱陈强, 贾宁, 马寿峰. 基于轨迹数据的车辆跟驰行为分析与建模综述[J]. 交通运输系统工程与信息, 2021, 21(5): 148-159.
[5]	张伟斌, 张蒲璘, 苏子毅, 孙锋. 基于自注意力机制与图自编码器的路网交通流数据修复模型[J]. 交通运输系统工程与信息, 2021, 21(4): 90-98.
[6]	李桃迎，王婷，张羽琪. 考虑多特征的高速公路交通流预测模型[J]. 交通运输系统工程与信息, 2021, 21(3): 101-111.
[7]	张文松，姚荣涵. 基于时空特性和组合深度学习的交通流参数估计[J]. 交通运输系统工程与信息, 2021, 21(1): 82-89.
[8]	李霞，汪一戈，崔洪军，朱敏清，王欣桐. 智能网联环境下复杂异质交通流稳定性解析[J]. 交通运输系统工程与信息, 2020, 20(6): 114-120.
[9]	梁军，钱晨阳，陈龙，王文飒，赵彤阳. 智能网联车辆混行交通流中灯语意图识别模型研究[J]. 交通运输系统工程与信息, 2020, 20(5): 36-44.
[10]	陈喜群，周凌霄，曹震 . 基于图卷积网络的路网短时交通流预测研究[J]. 交通运输系统工程与信息, 2020, 20(4): 49-55.
[11]	李嫚嫚，陆建，孙加辉. 多类型信息下的网络交通流演化模型[J]. 交通运输系统工程与信息, 2020, 20(4): 97-105.
[12]	陈华伟，邵毅明，敖谷昌，张惠玲. 研究降雨事件对交通流时空特性的影响[J]. 交通运输系统工程与信息, 2020, 20(4): 244-252.
[13]	陆文琦，芮一康，冉斌，谷远利. 智能网联环境下基于混合深度学习的交通流预测模型[J]. 交通运输系统工程与信息, 2020, 20(3): 47-53.
[14]	刘小明，田玉林，唐少虎，尚春琳，魏路. 基于时延特性建模的多断面短时交通流预测[J]. 交通运输系统工程与信息, 2020, 20(3): 54-60.
[15]	陈丹，尹嘉男. 航路扇区容量需求的交通流动力学推演与预测[J]. 交通运输系统工程与信息, 2020, 20(3): 206-211.