Car-following model and optimization strategy for connected and automated vehicles under mixed traffic environment

PENG Jia-li; SHANGGUAN Wei; CHAI Lin-guo; QIU Wei-zhi

doi:10.19818/j.cnki.1671-1637.2023.03.018

Volume 23 Issue 3

Jun. 2023

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PENG Jia-li, SHANGGUAN Wei, CHAI Lin-guo, QIU Wei-zhi. Car-following model and optimization strategy for connected and automated vehicles under mixed traffic environment[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 232-247. doi: 10.19818/j.cnki.1671-1637.2023.03.018

Citation:

PENG Jia-li, SHANGGUAN Wei, CHAI Lin-guo, QIU Wei-zhi. Car-following model and optimization strategy for connected and automated vehicles under mixed traffic environment[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 232-247. doi: 10.19818/j.cnki.1671-1637.2023.03.018

Citation:

PENG Jia-li, SHANGGUAN Wei, CHAI Lin-guo, QIU Wei-zhi. Car-following model and optimization strategy for connected and automated vehicles under mixed traffic environment[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 232-247. doi: 10.19818/j.cnki.1671-1637.2023.03.018

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Car-following model and optimization strategy for connected and automated vehicles under mixed traffic environment

doi: 10.19818/j.cnki.1671-1637.2023.03.018

1.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

Funds:

National Natural Science Foundation of China 52272328

Joint Fund of the Ministry of Education for Equipment Preresearch 8091B022238

Natural Science Foundation of Beijing L211022

More Information

Author Bio:
PENG Jia-li(1997-), male, doctoral student, jialipeng@bjtu.edu.cn

SHANGGUAN Wei(1979-), male, professor, PhD, wshg@bjtu.edu.cn
Received Date: 2022-11-30
Available Online: 2023-07-07
Publish Date: 2023-06-25

Abstract

Abstract

To improve the driving efficiency and traffic flow stability of vehicles in the mixed traffic environment, the informations such as the velocities and accelerations of multiple vehicles in front were fused, and an exponential smoothing approach was adopted to build a car-following model of connected and automated vehicles (CAVs) based on the backward-looking effect. Then the effects of number of vehicles in front and behind and the completeness of state information on the model stability were studied. The linear stability analysis was carried out and the optimal parameters of the model were determined by combining the Lyapunov's first method and linear harmonic perturbation method. Combined with the characteristics of mixed traffic environments, the car-following strategies of CAVs in different positions and states were proposed in the condition of communication information loss. The numerical simulations were carried out in three typical scenarios with different CAV penetration rates, including vehicle starting, vehicle braking to stop, and circular road. Research results show that in the scenario of vehicle braking to stop, the maximum stopping wave speed of all vehicles increases by 26.1%. In the scenario of vehicle starting, the maximum starting wave speed increases by 15.5%, and the accelerations and speeds of vehicles change more smoothly. In the circular road scenario, when the CAV penetration rate in the mixed traffic flow increases from 40% to 100%, the fluctuation time of average speed of vehicles in the larger disturbance scenario decreases by 44.8%, the wave peak decreases by 5.7%, and the wave trough increases by 19.4%, compared to the low CAV penetration rate scenarios. However, the proposed optimization strategy does not significantly improve the mixed traffic flow at a lower CAV penetration rate. Thus, in the current situation where it is difficult to construct an actual mixed traffic environment and conduct CAV real vehicle tests, the model and strategies can be employed for the car-following simulation and test verification in specific scenarios to effectively guarantee the absorption of traffic flow disturbance and stable driving vehicles in the mixed traffic environment.
- traffic information engineering,
- car-following model,
- numerical simulation,
- mixed traffic environment,
- connected and automated vehicle

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References

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Car-following model and optimization strategy for connected and automated vehicles under mixed traffic environment

doi: 10.19818/j.cnki.1671-1637.2023.03.018

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