智能网联车辆跟驰模型及交通流特性研究

Research on Car-following Model and Traffic Flow Characteristics of Intelligent and Connected Vehicle

智能网联车辆(Intelligent and Connected Vehicle,ICV)跟驰模型是研究ICV交通流特性的基础模型,针对ICV跟驰模型的建模及交通流特性开展研究。分析现有ICV跟驰模型存在的缺陷,考虑模型结构简洁且参数物理意义明确的特性,提出新的ICV跟驰模型。理论推导本文ICV跟驰模型以及原跟驰模型的基本图函数关系式,对比分析通行能力的影响,考虑周期性边界条件,设计数值仿真实验,仿真分析ICV的混入对混合交通流稳定性的影响。研究结果表明:本文所提ICV跟驰模型能够克服原模型通行能力受恒定车头时距影响较大的缺陷,相比原模型而言,本文ICV跟驰模型通行能力随自由流速度的变化幅度较显著,与实际车流运行特性相符合,同时,ICV有利于混合交通流稳定性的提升,当ICV比例达到60%时,混合交通流由不稳定状态转变为稳定状态。研究结果论证了所提ICV跟驰模型具备良好的模型特性,可为智能网联环境下的交通流特性分析提供模型参考。

The Intelligent Connected Vehicle(ICV)car-following model is a basic model for studying the characteristics of ICV traffic flow.In the paper,study is carried out on the modeling of the ICV carfollowing model and traffic flow characteristics.The shortcomings of the existing ICV car-following model is analyzed,and a new ICV car-following model is proposed by referring to the characteristics of the model’s concise structure and clear physical meaning of the parameters.Fundamental diagram models ofICV car-following model and original car-following model are deduced theoretically,and the impact on traffic capacity is also compared.Considering periodic boundary conditions,numerical simulation experiment is designed to analyze the influence of ICV on stability of mixed traffic flow.Research indicates that:traffic capacity of original model is greatly affected by the constant headway.The ICV car-following model proposed in this paper can overcome the disadvantage.Compared with the original model,the capacity of the ICV car-following model in this paper varies significantly with the free flow velocity,which is consistent with the actual traffic characteristics.At the same time,ICV is conducive to improving the stability of mixed traffic flow.When the ICV rate increases to 60%,mixed traffic flow changes from unstable state to stable state.The research results demonstrate that the proposed ICV car-following model has good model characteristics,which can provide a model reference for the analysis of traffic flow characteristics in the intelligent network environment.