ABSTRACT

The imputation of time series traffic flow data is of great significance to the intelligent transportation, urban planning, and road emergency handling. This paper proposes a filling missing time series traffic data with Generative Adversarial Network (ST-FVGAN), which not only considers the spatio-temporal correlation and utilizes the idea of data generating of the Generative Adversarial Network, but also considers the external factors and introduces a more comprehensive loss function. Specifically, the model firstly constructs a Generator network which is composed of convolutional layer, residual block, and pixelshuffle block for the better potential distribution of the existing data, and then use the Discriminator network for the input judging. Experiments are conducted on the open-source TaxiBJ GPS dataset, and evaluated by the root mean square error (RMSE) index. The experimental results show that our model has the better accurate and reasonable performance than the traditional imputation methods

摘要

时序交通流数据的插补对于智能交通，城市规划和道路应急处理具有重要意义。本文提出了利用生成对抗网络（ST-FVGAN）填充缺失时间序列交通数据的方法，该模型不仅考虑时空相关性，并利用生成对抗网络的数据生成思想，还考虑了外部因素，并介绍了更全面的损失功能。具体来说，该模型首先构造一个由卷积层，残差块和像素混合块组成的生成器网络，以更好地分布现有数据，然后使用鉴别器网络进行输入判断。实验是在开源TaxiBJ GPS数据集上进行的，并通过均方根误差（RMSE）指数进行评估。实验结果表明，与传统的插补方法相比，该模型具有更好的精度和合理性。