Traffic state prediction plays an important role in intelligent transportation systems, but the complex spatial influence of traffic networks and the non-stationary temporal nature of traffic states make it a challenging task. In this study, a new traffic network state prediction model for freeways based on a generative adversarial framework is proposed. The generator based on the long short-term memory networks is adopted to generate future traffic states, and a discriminator with multiple fully connected layers is applied to simultaneously ensure the prediction accuracy. The results of experiments show that the proposed framework can effectively predict future traffic network states and is superior to the baselines.

中文翻译：

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基于生成对抗网络的道路交通网络状态预测

交通状态预测在智能交通系统中起着重要的作用，但是交通网络的复杂空间影响和交通状态的非平稳时间特性使其成为一项具有挑战性的任务。在这项研究中，提出了一种基于生成对抗框架的高速公路交通网络状态预测模型。采用基于长短期记忆网络的生成器来生成未来的流量状态，并应用具有多个全连接层的鉴别器以同时确保预测精度。实验结果表明，所提出的框架可以有效地预测未来的交通网络状态，并且优于基线。