基于鱼群效应的智能网联车队形成与演化机理研究

Formation and Evolution Mechanism of Connected and Autonomous Fleet Based on Fish Streaming Effect

随着智能网联车的快速发展,智能网联车与人工驾驶车辆混行下的交通特性分析和协同控制研究已成为热点。本文构建多车道混合交通流元胞自动机仿真模型,对智能网联车队的形成及状态演化过程进行仿真刻画。首先,基于智能网联车的网联化特征,引入鱼群效应来描述4种智能网联车队的形成过程;其次,从车队角度,利用马尔可夫性计算车队规模转移概率,阐述车队状态演化过程;再次,引入Gipps安全距离原则对NaSch模型进行改进,并对智能网联车和智能网联车队进行速度引导;最后,基于实测的车辆到达率,对建立的基于鱼群效应的混合车流元胞自动机模型进行仿真实验。结果表明:智能网联车队可以有效改善混合交通流的运行状态,缓解交通拥堵;在渗透率60%的条件下,智能网联车队规模为3时,与不存在车队相比,拥堵率可减少43.9%,交通流速度约提升43%,并且随着车队规模的增大,交通流平均速度趋于平稳。

With the rapid development of connected and autonomous vehicles(CAV),the research on traffic characteristics and cooperative control of the intelligent mixed traffic that is composed of CAVs and human-driven vehicles,has become a research focus.In this paper,a multi-lane cellular automata model for the mixed traffic is established to simulate the formation and evolution process of a CAV fleet.Firstly,the fish streaming effect is introduced to describe the formation process of four kinds of CAV fleets based on their networked characteristics.Secondly,the Markov property is used to calculate the fleet scale transfer probability from the perspective of the fleet,and the evolution process of the CAV fleet state is described.Thirdly,the rule of Gipps safety distance is introduced to improve the NaSch model,and CAV vehicles and fleet are subjected to the speed guidance.Finally,this paper carries out simulation experiments on the established mixed traffic flow cellular automata model based on fish streaming according to the measured vehicle arrival rate.The results show that the CAV fleet can effectively improve the operating state of mixed traffic and alleviate traffic congestion;Under the condition of a 60%penetration rate,the congestion rate can be reduced by 43.9%when the CAV fleet scale is 3 compared with the non-fleet,the traffic flow speed can be increased about 43%,and the average speed tends to be stable with the increase of fleet scale.