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.

交通流预测是交通控制和制导系统中的基本问题之一，近年来随着AI成功为新方法所吸引，越来越引起人们的关注。在本文中，我们提出了一种新的模型，即用于时间序列预测的自我注意生成对抗网络（SATP-GAN）。SATP-GAN方法基于自我注意和生成对抗网络（GAN）机制，该机制由GAN模块和强化学习（RL）模块组成。在GAN模块中，我们应用自注意力层来捕获时间序列数据的模式，而不是RNN（递归神经网络）。在RL模块中，我们应用RL算法来调整SATP-GAN模型的参数。我们在现实世界的交通数据集上评估了框架，并获得了6的一致改进。比基准方法高5％。SATP-GAN框架证明，在微调参数后，GAN机制也可用于时间序列预测。