SATP-GAN: self-attention based generative adversarial network for traffic flow prediction
Liang Zhang, Jianqing Wu, Jun Shen, Ming Chen, Rui Wang, Xinliang Zhou, Cankun Xu, Quankai Yao, Qiang Wu
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 time-series data instead of RNNs (recurrent neural networks). In the RL module, we apply the RL algorithm to tune the parameters of our SATP-GAN model. We evaluate the framework on the real-world traffic dataset and obtain a consistent improvement of 6.5% over baseline methods. The SATP-GAN framework proves the GAN mechanism is also available for time-series prediction after fine-tuning the parameters.