"SATP-GAN: self-attention based generative adversarial network for traf" by Liang Zhang, Jianqing Wu et al.
Abstract Traffic flow prediction, one of the essential problems in traffic control and guidance systems, is still drawing increasing attention in recent years with the new methods tipped by the success of AI. In this paper, we propose a novel model, namely self-attention generative adversarial networks for time-series prediction (SATP-GAN). The SATP-GAN method is based on self-attention and generative adversarial networks (GAN) mechanisms, which are composed of the GAN module and reinforcement learning (RL) module. In the GAN module, we apply the self-attention layer to capture the pattern of...