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复杂系统与复杂性科学  2026, Vol. 23 Issue (2): 138-143    DOI: 10.13306/j.1672-3813.2026.02.017
  研究前沿 本期目录 | 过刊浏览 | 高级检索 |
智能跟驰交通元胞自动机模型
邓建华, 冯焕焕, 葛婷
苏州科技大学土木工程学院,江苏 苏州 215011
Intelligent Car-following Traffic Cellular Automaton Model
DENG Jianhua, FENG Huanhuan, GE Ting
College of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
全文: PDF(1454 KB)  
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摘要 为提高交通元胞自动机(TCA)模型的微观驾驶行为分辨度,提出TCA中嵌入智能驾驶模型(IDM)的方法,形成新的智能跟驰交通元胞自动机(ICTCA)模型。定义元胞空间粒度基础上,设计摄动实验,模拟队列驶离路口受小扰动的跟驰迟滞行为,结果发现:新模型具有分辨跟驰迟滞能力,且空间粒度越细越能获得稳定迟滞效应。说明:合适空间粒度下,该模型具有接近连续跟驰模型的微观驾驶行为分辨度。因继承了IDM与TCA的特征,新模型有适用于智能网联场景的更多可能。
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邓建华
冯焕焕
葛婷
关键词 智能交通元胞自动机IDM元胞空间粒度跟驰迟滞    
Abstract:In order to improve the micro driving behavior resolution of a traffic cellular automaton(TCA) model, an intelligent driving model(IDM) embedded in TCA was proposed to form a new intelligent car-following traffic cellular automaton(ICTCA) model. Based on the definition of cell-spatial granularity, perturbation experiments were designed to simulate the car-following hysteresis behavior of a platoon leaving the intersection with small disturbances. The results showed that the new model has the ability to resolve the car-following hysteresis, and the finer the cell-spatial granularity, the more stable hysteresis effect can be obtained, indicating that the proposed model has the micro driving behavior resolution close to that of the continuous car-following model under appropriate cell-spatial granularity. Inheriting the characteristics of IDM and TCA, the new model has more possibilities to be applied to intelligent networking scenarios.
Key wordsintelligent transportation    cellular automaton    IDM    cell-spatial granularity    car-following hysteresis
收稿日期: 2024-04-29      出版日期: 2026-05-19
:  U491.2  
基金资助:国家自然科学基金(51808370),苏州科技大学基金(341311108; XKQ201305)
作者简介: 邓建华(1972-),男,湖南永兴人,硕士,副教授,主要研究方向为交通复杂系统仿真。
引用本文:   
邓建华, 冯焕焕, 葛婷. 智能跟驰交通元胞自动机模型[J]. 复杂系统与复杂性科学, 2026, 23(2): 138-143.
DENG Jianhua, FENG Huanhuan, GE Ting. Intelligent Car-following Traffic Cellular Automaton Model[J]. Complex Systems and Complexity Science, 2026, 23(2): 138-143.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2026.02.017      或      https://fzkx.qdu.edu.cn/CN/Y2026/V23/I2/138
[1] ZHANG T T, JIN P J, MCQUADE S T, et al. Car-following models: a multidisciplinary review[J]. IEEE Transactions on Intelligent Vehicles, 2024, 10(1): 1-26.
[2] 邱小平, 于丹, 孙若晓, 等. 基于安全距离的元胞自动机交通流模型研究[J]. 交通运输系统工程与信息, 2015, 15(2): 54-60.
QIU X P,YU D, SUN R X, et al. Cellular automata model based on safety distance[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(2): 54-60.
[3] YOU S, ZHOU Y P. Optimization driven cellular automata for traffic flow prediction at signalized intersections[J]. Journal of Intelligent & Fuzzy Systems, 2021, 40: 1547-1566.
[4] WANG Z, SHI Y Y, TONG W P, et al. Car-following models for human-driven vehicles and autonomous vehicles: a systematic review[J]. Journal of Transportation Engineering, Part A: Systems, 2023, 149(8): 04023075.
[5] NEWELL G F. Theories of instability in dense highway traffic[J]. The Operations Research Society of Japan, 1965, 1(5): 9-54.
[6] CHOWDHURY D, SANTEN L, SCHADSCHNEIDER A. Statistical physics of vehicular traffic and some related systems[J]. Physics Reports, 2000, 329(4): 199-329.
[7] NAGEL K, SCHRECKENBERG M. A cellular automaton model for freeway traffic[J]. Journal De Physique I, 1992, 2(12): 2221-2229.
[8] 贾宁, 马寿峰. 最优速度模型与元胞自动机模型的比较研究[J]. 物理学报, 2010, 59(2): 832-841.
JIA N, MA S F. Comparison between the optimal velocity model and the Nagel-Schreckenberg model[J]. Acta Physica Sinica,2010, 59(2): 832-841.
[9] KRAUSS S, WAGNER P, GAWRON C. Continuous limit of the Nagel-Schreckenberg model[J]. Physical Review E, 1996, 54(4): 3707-3712.
[10] CORLI A, FAN H. Hysteresis and stop-and-go waves in traffic flows[J]. Mathematical Models and Methods in Applied Sciences, 2019, 29(14): 2637-2678.
[11] ZHANG H M. A mathematical theory of traffic hysteresis[J]. Transportation Research Part B-Methodological, 1999, 33(1): 1-23.
[12] MATTAS K, ALBANO G, DONA R, et al. On the relationship between traffic hysteresis and string stability of vehicle platoons[J]. Transportation Research Part B: Methodological, 2023, 174: 102785.
[13] TIAN J, ZHU C, CHEN D, et al. Car following behavioral stochasticity analysis and modeling: perspective from wave travel time[J]. Transportation Research Part B: Methodological, 2021, 143: 160-176.
[14] NEWELL G F. Nonlinear effects in the dynamics of car following[J]. Operations Research, 1961, 9(2): 209-229.
[15] 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.
[16] TREIBER M, HENNECKE A, HELBING D. Congested traffic states in empirical observations and microscopic simulations[J]. Physical Review E, 2000, 62(2): 1805-1824.
[17] 邓建华, 冯焕焕, 葛婷. 初始化对交通元胞自动机模型稳定性的影响[J]. 复杂系统与复杂性科学, 2023, 20(2): 105-110.
DENG J H, FENG H H, GE T. Influence of the initialization method on the stability of traffic cellular automata model[J]. Complex Systems and Complexity Science, 2023, 20(2): 105-110.
[18] TREIBER M, HELBING D. Explanation of observed features of self-organization in traffic flow[J]. Physics, 1999, 30(4): 311-317.
[19] MONTANINO M, MONTEIL J, PUNZO V. From homogeneous to heterogeneous traffic flows: Lp string stability under uncertain model parameters[J]. Transportation Research Part B: Methodological, 2021, 146: 136-154.
[20] BOUADI M, JIA B, JIANG R, et al. Stochastic factors and string stability of traffic flow: analytical investigation and numerical study based on car-following models[J]. Transportation Research Part B: Methodological, 2022, 165: 96-122.
[21] ZHOU Z, LI L, QU X, et al. A self-adaptive IDM car-following strategy considering asymptotic stability and damping characteristics[J]. Physica A: Statistical Mechanics and Its Applications, 2024, 637: 129539.
[22] YU Z, ZHAO J, JIANG R, et al. Theory-data dual driven car following model in traffic flow mixed of AVs and HDVs[J]. Transportation Research Part C: Emerging Technologies, 2024, 165: 104747.
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