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智能网联环境下信号交叉口车辆轨迹重构模型

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
车辆轨迹数据提供了大量的时空交通流信息,可用于各类交通研究.传统车辆轨迹模型多以人工驾驶环境为研究对象,普遍未考虑由常规车(RV)、网联人工驾驶车(CV)以及智能网联车(CAV)组成的混合交通流的影响.为解决该问题,构建智能网联环境下信号交叉口全样本车辆轨迹重构模型.首先,介绍并分析智能网联环境下城市道路交叉口处车辆组成及排队通过情况;然后,构建城市道路混合交通流轨迹数量估计模型,并针对前后车的排队情况提出虚拟车的概念,用于估计不同车辆的交通状态;最后,设计数值仿真实验分析交通流密度和网联车渗透率对模型的影响,并基于NGSIM数据进行实例验证.结果表明:轨迹重构模型的数量误差和位置误差均随着交通流密度和网联车渗透率的增大而减小,如交通流密度由 20 veh/km增大至 50 veh/km的过程中,模型数量误差和位置误差均呈现下降趋势,且最大误差分别不超过 6.88%和 8.02 m;与网联人工驾驶车渗透率相比,智能网联车的渗透率对模型结果影响更大.
Vehicle Trajectory Reconstruction Model of Signalized Intersection in Connected Automated Environments
Vehicle trajectory data provides abundant spatial-temporal traffic flow information,which can be used for traffic research.Traditional vehicle trajectory models mostly focus on the artificial driving environment and fail to consider the impact of mixed traffic flows composed of regular vehicles(RVs),connected vehicles(CVs),and connected automated vehicles(CAVs).To solve this problem,a full sample vehicle trajectory reconstruction model of signalized intersections in connected automated environments was proposed.Firstly,the composition of vehicles at signalized intersections of urban roads and the passage of queues in connected automated environments were analyzed.Secondly,a model for estimating the number of trajectories of mixed traffic flows on urban roads was constructed,and the concept of virtual vehicles was further proposed to estimate the traffic status of different vehicles according to the queuing of front and rear vehicles.Finally,a numerical simulation test was designed to analyze the influence of traffic flow density and penetration rate of CAVs and CVs on the model,and the model was verified by NGSIM data.The results show that the error of the number and position of the model decreases with the increase in traffic flow density and the penetration rate of CAVs and CVs.For example,when the traffic flow density increases from 20 veh/km to 50 veh/km,both the error of the number and position of the model shows a decreasing trend,and the maximum error is no more than 6.88%and 8.02 m.Compared with that of CVs,the penetration rate of CAVs has a greater impact on the model results.

intelligent transportationcar-following modeltraffic wave theoryconnected automated vehiclemixed traffic flowsignalized intersection

杨涛、马玉琴、刘梦、姚志洪、蒋阳升

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中铁第一勘察设计院集团有限公司,陕西 西安 710043

西南交通大学交通运输与物流学院,四川 成都 611756

中南勘察设计院集团有限公司,湖北武汉 430074

西南交通大学综合交通大数据应用技术国家工程实验室,四川成都 611756

西南交通大学综合交通运输智能化国家地方联合工程实验室,四川成都 611756

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智能交通 跟驰模型 交通波理论 智能网联车 混合交通流 信号交叉口

四川省科技计划项目四川省科技计划项目中央高校基本科研业务费专项资金资助项目中铁第一勘察设计院集团有限公司科研项目

2021YJ05352022YFG01522682021CX058院科17-48

2024

10.3969/j.issn.0258-2724.20220321

西南交通大学学报
西南交通大学

西南交通大学学报

CSTPCD北大核心
影响因子：0.973
ISSN：0258-2724
年,卷(期)：2024.59(5)
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