Trajectory Reconstruction for Traffic Flow Mixed withConnected Automated Vehicles Based on Car-Following Characteristics

被引：0
|
作者：
Jiang Y. [1 ,2 ]
Liu M. [1 ,2 ]
Wang S. [1 ,2 ]
Gao K. [1 ,2 ]
Yao Z. [1 ,2 ]
机构：
[1] School of Transportation and Logistics, Southwest Jiaotong University, Chengdu
[2] National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu
来源：
| 1600年 / Science Press卷 / 56期
关键词：
Car-following model; Connected automated vehicle; Intelligent transportation system (ITS); Mixed traffic flow;
D O I：
10.3969/j.issn.0258-2724.20200735
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Department of Journal of Southwest Jiaotong University. All right reserved.
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页码：1135 / 1142
页数：7
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