IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data
IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data
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DOI：
10.1109/access.2021.3103456
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Meidani, Hadi
中科院分区：
文献类型：
--
作者：
Kazemi, Amir;Meidani, Hadi
在智能交通系统中越来越多地使用传感器数据需要准确的插补算法，以便在偶尔缺乏数据的情况下实现可靠的交通管理。作为有效的插补方法之一，生成对抗网络（GAN）是可用于数据插补的隐式生成模型，它被表述为无监督学习问题。这项工作引入了一种新颖的迭代 GAN 架构，称为用于插补的迭代生成对抗网络 (IGANI)，用于数据插补。 IGANI 分两步对数据进行插补，并保持生成插补器的可逆性，这将被证明是所提出的基于 GAN 的插补收敛的充分条件。我们提出的方法的性能评估基于（1）中国广州市收集的交通速度数据的插补，以及使用插补数据训练短期交通预测模型，以及（2）波特兰-温哥华大都市区高速公路的多变量交通数据的插补，其中包括交通量、占用率和速度，每个数据都有不同的缺失率。结果表明，与之前基于 GAN 的插补架构相比，我们提出的算法大多能产生更准确的结果。
Increasing use of sensor data in intelligent transportation systems calls for accurate imputation algorithms that can enable reliable traffic management in the occasional absence of data. As one of the effective imputation approaches, generative adversarial networks (GANs) are implicit generative models that can be used for data imputation, which is formulated as an unsupervised learning problem. This work introduces a novel iterative GAN architecture, called Iterative Generative Adversarial Networks for Imputation (IGANI), for data imputation. IGANI imputes data in two steps and maintains the invertibility of the generative imputer, which will be shown to be a sufficient condition for the convergence of the proposed GAN-based imputation. The performance of our proposed method is evaluated on (1) the imputation of traffic speed data collected in the city of Guangzhou in China, and the training of short-term traffic prediction models using imputed data, and (2) the imputation of multi-variable traffic data of highways in Portland-Vancouver metropolitan region which includes volume, occupancy, and speed with different missing rates for each of them. It is shown that our proposed algorithm mostly produces more accurate results compared to those of previous GAN-based imputation architectures.