| Popis: |
Traffic signal control is an important part of intelligent transportation. Efficient traffic signal control strategies not only alleviate traffic congestion, improve vehicle traffic efficiency, but also reduce exhaust pollution during the waiting period. The traditional multiple intersection signal light control method are generally faced with passive control and can not adapt to complex changes. In view of this, the study first combines reinforcement learning and Markov decision to construct a signal control problem model for multiple intersections. Secondly, deep Q-learning networks are introduced for decision solving, and multi-head attention mechanisms and graph convolutional networks are further introduced for optimization and improvement. Finally, a spatial lightweight model for multiple intersection adaptive graph convolution is proposed. The experimental results show that the maximum throughput of the multi-intersection adaptive control model can reach 195 vehicles/min, the shortest queue length is 4.31 vehicles, the shortest average delay is 1.23 minutes, the shortest average travel time is 2.07 minutes, and the attention value of adjacent intersections is close to 80%. The average green wave time under this model is 4 minutes, and the longest green wave section can reach 5 minutes. It can be seen that the new multi-intersection signal adaptive control model proposed in this study has advantages and feasibility compared with similar methods in various indexes, and can provide new ideas and methods for the further development of traffic signal automatic control technology. |
