• ISSN 0258-2724
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Volume 56 Issue 6
Dec.  2021
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Journal of Southwest Jiaotong University  > 2021  >  56(6): 1135-1142.
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. doi: 10.3969/j.issn.0258-2724.20200735
Citation: 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. doi: 10.3969/j.issn.0258-2724.20200735
shu

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

doi: 10.3969/j.issn.0258-2724.20200735
  • 1.

    School of Transportation and Logistics，Southwest Jiaotong University，Chengdu 611756，China

  • 2.

    National Engineering Laboratory of Integrated Transportation Big Data Application Technology，Southwest Jiaotong University，Chengdu 611756，China

  • Received Date: 10 Nov 2020
  • Rev Recd Date: 13 Jan 2021
    • Available Online: 15 Apr 2021
  • Publish Date: 15 Apr 2021
    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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