Road network pixelization: A traffic flow imputation method based on image restoration techniques

Abstract

The complete traffic network data is crucial for accurate traffic data prediction in intelligent transportation systems. Inspired by the success of Generative Adversarial Networks (GANs) in image restoration, this study proposes a traffic flow imputation method by employing image restoration techniques. First, we propose a trajectory data representation method called Trajectory2Matrix, that converts the trajectory data into a two-dimensional spatiotemporal relation feature map. Consequently, the data imputation scale and scope are increased. Second, a spatiotemporal feature map generation module based on a graph convolutional network is designed to optimize the GAN generator, thus utilizing its advantages for non-Euclidean data and dynamic spatiotemporal correlation. Finally, a heterogeneous multisource data fusion module based on a channel attention mechanism is proposed to merge dynamic/static external attributes and multimode characteristics in time. The proposed method improved imputation accuracy and robustness for multitype missing data patterns, especially in high missing rate situations.