论文标题
深度卷积生成的对抗网络，用于流量数据归档编码时间序列作为图像
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images
论文作者
论文摘要
足够的高质量流量数据是与拥塞预测，速度预测，事件检测和其他交通运营任务有关的各种智能运输系统（ITS）应用程序（ITS）应用和研究的关键组成部分。尽管如此，丢失的流量数据是传感器数据中的一个常见问题，这是由于多种原因，例如故障，维护或校准不良以及间歇性通信。这种缺失的数据问题通常会使数据分析和决策变得复杂且具有挑战性。在这项研究中，我们开发了基于生成的对抗网络（GAN）的流量传感器数据插图框架（TSDIGAN），以通过生成逼真的合成数据来有效地重建缺失的数据。近年来，甘斯在图像数据生成方面表现出了令人印象深刻的成功。但是，通过利用基于GAN的建模来生成流量数据是一项具有挑战性的任务，因为流量数据具有强大的时间依赖性。为了解决这个问题，我们提出了一种称为Gramian Angular求和场（GASF）的新型时间相关编码方法，该方法将流量时间序列数据生成的问题转换为图像生成的问题。我们已经使用CalTrans性能管理系统（PEMS）提供的基准数据集对我们提出的模型进行了评估并测试了我们的模型。这项研究表明，与基准数据集中的最新模型相比，提出的模型可以显着提高流量数据归合精度（MAE）和均方根误差（RMSE）。此外，即使在很高的数据速率（$> $ 50 \％）下，该模型也达到了归纳任务的准确性，这表明了所提出的模型的稳健性和效率。
Sufficient high-quality traffic data are a crucial component of various Intelligent Transportation System (ITS) applications and research related to congestion prediction, speed prediction, incident detection, and other traffic operation tasks. Nonetheless, missing traffic data are a common issue in sensor data which is inevitable due to several reasons, such as malfunctioning, poor maintenance or calibration, and intermittent communications. Such missing data issues often make data analysis and decision-making complicated and challenging. In this study, we have developed a generative adversarial network (GAN) based traffic sensor data imputation framework (TSDIGAN) to efficiently reconstruct the missing data by generating realistic synthetic data. In recent years, GANs have shown impressive success in image data generation. However, generating traffic data by taking advantage of GAN based modeling is a challenging task, since traffic data have strong time dependency. To address this problem, we propose a novel time-dependent encoding method called the Gramian Angular Summation Field (GASF) that converts the problem of traffic time-series data generation into that of image generation. We have evaluated and tested our proposed model using the benchmark dataset provided by Caltrans Performance Management Systems (PeMS). This study shows that the proposed model can significantly improve the traffic data imputation accuracy in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to state-of-the-art models on the benchmark dataset. Further, the model achieves reasonably high accuracy in imputation tasks even under a very high missing data rate ($>$ 50\%), which shows the robustness and efficiency of the proposed model.
