Abstract:

As the technology of ubiquitous Internet of things and artificial intelligence improves by leaps and bounds, the transportation system revolution is flourishing and bringing new opportunities and challenges.Considering the defect in the existing navigation system, and the neglect of the temporal and spatial characteristics of traffic flow, the macro traffic network and micro vehicle network were modeled and their coupling relationship was mined.Then, a digital twin based urban traffic forecasting and guidance method was proposed to alleviate the problem of traffic congestion.The spatial-temporal traffic flow information was predicted through the diffusion convolution recurrent neural network, which was explicitly applied to the vehicle path planning decision.On this basis, a spatial-temporal collaborative deep reinforcement learning method was proposed to implement the future-oriented collaborative path planning of vehicles.It also guided the underlying vehicle twins to select the optimal strategy for the real world.With SUMO for simulation verification, the experimental results show that the proposed method is significantly better than the existing algorithms in improving the travel completion ratio and congestion relief, and can improve the efficiency of urban traffic travel.

Key words: digital twin, traffic congestion, deep reinforcement learning, traffic flow forecasting and guidance, diffu-sion convolution