  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.
