  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.
