Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks (GANs) to enhance traffic data imputation. Parallel data is a recently proposed method of using synthetic and real data for data mining and data-driven process, in which we apply GANs to generate synthetic traffic data. As it is difficult for the standard GAN algorithm to generate time-dependent traffic flow data, we made twofold modifications: 1) using the real data or the corrupted ones instead of random vectors as latent codes to generator within GANs and 2) introducing a representation loss to measure discrepancy between the synthetic data and the real data. The experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic data imputation.

中文翻译：

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使用并行数据和生成对抗网络的交通流插补

交通数据插补对于智能交通系统的研究和应用都至关重要。为了开发高精度的交通数据插补模型，交通数据必须是庞大而多样的，成本高昂。另一种方法是使用合成交通数据，它既便宜又易于访问。在本文中，我们提出了一种使用并行数据和生成对抗网络 (GAN) 来增强交通数据插补的新方法。并行数据是最近提出的使用合成和真实数据进行数据挖掘和数据驱动过程的方法，其中我们应用 GAN 来生成合成交通数据。由于标准 GAN 算法难以生成时间相关的交通流数据，我们做了两处修改：1) 使用真实数据或损坏的数据而不是随机向量作为潜在代码在 GAN 中生成，2) 引入表示损失来衡量合成数据和真实数据之间的差异。在真实交通数据集上的实验结果表明，我们的方法可以显着提高交通数据插补的性能。