Volume 22 Issue 3
Jun.  2022
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SHANGGUAN Wei, PANG Xiao-yu, LI Qiu-yan, CHAI Lin-guo. Guidance and cooperative operation method for group vehicles in vehicle-infrastructure cooperative environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 68-78. doi: 10.19818/j.cnki.1671-1637.2022.03.005
Citation: SHANGGUAN Wei, PANG Xiao-yu, LI Qiu-yan, CHAI Lin-guo. Guidance and cooperative operation method for group vehicles in vehicle-infrastructure cooperative environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 68-78. doi: 10.19818/j.cnki.1671-1637.2022.03.005
shu

Guidance and cooperative operation method for group vehicles in vehicle-infrastructure cooperative environment

doi: 10.19818/j.cnki.1671-1637.2022.03.005
  • 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 Key Research and Development Program of China 2018YFB1600600

Open Research Fund for Key Laboratory of Intelligent Operation of Civil Aviation Airport Group KLAGI020180901

National Natural Science Foundation of China 61773049

More Information
  • Author Bio:

    SHANGGUAN Wei(1979-), male, professor, PhD, wshg@bjtu.edu.cn

  • Received Date: 2021-12-16
  • Publish Date: 2022-06-25
    Abstract
  • To solve the traffic congestion problem caused by urban development, explore the potential of road traffic, and improve the driving efficiency of vehicles in the road network in vehicle-infrastructure cooperative environments, a guidance optimization method and a cooperative contral strategy for group vehicles were proposed. For the vehicle guidance allocation, the group vehicles allocation rules based on the road saturation, vehicle travel time, and delay were designed with the goals of optimal traffic efficiency and minimum vehicle emissions by the feasible path between the starting point and the destination. An optimization model for the group vehicles guidance allocation was built and solved by the multi-objective non-dominated sorting genetic algorithms-Ⅱ (NSGA-Ⅱ) and the multi-objective particle swarm optimization algorithm. Regarding the strategy for the vehicle cooperative operation control, a multi-vehicle cooperative operation model based on the idea of the gravitational field was created, and a multi-vehicle cooperative acceleration and deceleration strategy was proposed. The results of vehicle guidance optimization under different penetration rates of connected and automated vehicle (CAV) were compared through the simulation verification. The vehicle cooperative acceleration and deceleration strategy was simulated, and the guidance optimization method and the cooperative control strategy were co-simulated. Simulation results show that the multi-objective guidance allocation method can improve the vehicle speed and environmental benefits, and the average speed of the group vehicles is positively correlated with the CAV penetration rate. In the four-car group driving environment, the cooperative acceleration and deceleration strategy can increase the initial average acceleration of the vehicle by 15.0% and 8.2% respectively, during the acceleration and deceleration. The vehicle can quickly reach the target speed, and the safety of the vehicles can thereby be ensured. In the co-simulation environment, the accelerations of the group vehicles in the road network increase by 11.6% on average, their speeds increase by 1.6% on average, and their carbon-oxygen compound emissions reduce by about 4.9%. Therefore, the proposed method can be employed to improve the traffic efficiency of the road network, reduce the energy consumption of vehicles, and lower the adverse impact on the environment. 2 tabs, 10 figs, 31 refs.

     

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