| 基于跟驰特性的智能网联车混合交通流轨迹重构 |
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| 引用本文： | 蒋阳升,刘梦,王思琛,高宽,姚志洪.基于跟驰特性的智能网联车混合交通流轨迹重构[J].西南交通大学学报,2021,56(6):1135-1142. |
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| 作者姓名： | 蒋阳升 刘梦 王思琛 高宽 姚志洪 |
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| 基金项目： | 国家自然科学基金（52002339）；四川省科技计划（2021YJ0535，2020YFH0026）；广西省科技计划（2021AA01007AA）；中央高校基本科研业务费专项资金（2682021CX058） |
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| 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.
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| 关 键 词： | 智能交通 跟驰模型 智能网联车 混合交通流 |
| 收稿时间： | 2020-11-10 |
Trajectory Reconstruction for Traffic Flow Mixed withConnected Automated Vehicles Based on Car-Following Characteristics |
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| JIANG Yangsheng,LIU Meng,WANG Sichen,GAO Kuan,YAO Zhihong.Trajectory Reconstruction for Traffic Flow Mixed withConnected Automated Vehicles Based on Car-Following Characteristics[J].Journal of Southwest Jiaotong University,2021,56(6):1135-1142. |
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| Authors: | JIANG Yangsheng LIU Meng WANG Sichen GAO Kuan YAO Zhihong |
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| Abstract: | 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. |
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