摘要
车辆轨迹数据蕴含着丰富的时空交通信息,是交通状态估计的基础数据之一.为解决现有数据采集环境难以获得全样本车辆轨迹的问题,面向智能网联环境,构建了混合交通流全样本车辆轨迹重构模型.首先,分析了智能网联环境下混合交通流的车辆构成及其轨迹数据采集环境;然后,提出了基于智能驾驶员跟驰模型的车辆轨迹重构模型,实现了对插入轨迹数量、轨迹位置和速度等参数的估计;最后,设计仿真试验验证了模型在不同交通流密度和智能网联车(connected automated vehicle,CAV)渗透率条件下的适用性.试验结果表明:CAV和网联人工驾驶车(connected vehicle,CV)的渗透率为8%和20%时,该车辆轨迹重构模型在不同交通流密度下均能重构84%以上的车辆轨迹;重构轨迹准确性随着CAV和CV渗透率的增加而提高;当交通密度为70辆/km,且CAV渗透率仅为4%的情况下,模型也能重构82%的车辆轨迹.
Vehicle trajectory data contains massive spatial-temporal traffic information,which is one of the necessary data for traffic state estimation.To solve the problem that it's difficult to obtain the fully sampled vehicular trajectory in the existing data collection environment,oriented to the connected and automated environment,a fully sampled trajectory reconstruction model of mixed traffic flow is proposed.Firstly,vehicle composition and trajectory data collection environment of mixed traffic flow with the connected automated vehicle(CAV)are analyzed.Then,a vehicle trajectory reconstruction model is proposed based on intelligent driver car-following model.Based on this,the number of inserted trajectories,trajectory position and speed are estimated.Finally,numerical simulation is designed to investigate the influence of traffic density and penetration rate of CAVs.Results show that,when the penetration rates of CAV and connected vehicle(CV)at 8%and 20%,respectively,the model can reconstruct more than 84%vehicular trajectories under different traffic densities.The accuracy of the reconstructed trajectories increases with the increase in penetration rates of CAV and CV.Besides,when the traffic density is 70 veh/km and the penetration rate of CAV at a low level of 4%,the proposed model can reconstruct 82%vehicular trajectories.
作者
蒋阳升
刘梦
王思琛
高宽
姚志洪
JIANG Yangsheng;LIU Meng;WANG Sichen;GAO Kuan;YAO Zhihong(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 611756,China;National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Southwest Jiaotong University,Chengdu 611756,China)
出处
《西南交通大学学报》
EI
CSCD
北大核心
2021年第6期1135-1142,F0002,共9页
Journal of Southwest Jiaotong University
基金
国家自然科学基金(52002339)
四川省科技计划(2021YJ0535,2020YFH0026)
广西壮族自治区科技计划(2021AA01007AA)
中央高校基本科研业务费专项资金(2682021CX058)
西南交通大学综合交通大数据应用技术国家工程实验室交大数科创新中心项目(JDSKCXZX-202003)
重庆交通大学重庆市交通运输工程重点实验室开放课题(2018TE01)的资助.
关键词
智能交通
跟驰模型
智能网联车
混合交通流
intelligent transportation system(ITS)
car-following model
connected automated vehicle
mixed traffic flow
