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STGAN: Spatio-Temporal Generative Adversarial Network for Traffic Data Imputation by Ye Yuan; Yong Zhang; Boyue Wang; Yuan Peng; Yongli Hu; Baocai Yin is a Computer Science article available to read on EtoBox.

What is STGAN: Spatio-Temporal Generative Adversarial Network for Traffic Data Imputation about?

The traffic data corrupted by noise and missing entries often lead to the poor performance of Intelligent Transportation Systems (ITS), such as the bad congestion prediction and route guidance. How to efficiently impute the traffic data is an urgent problem. As a classic deep learning method, Generative Adversarial Network (GAN) achieves remarkable success in image recovery fields, which opens up a new way for the traffic data imputation. In this paper, we propose a novel spatio-temporal GAN model for the traffic data imputation (STGAN). Firstly, we design the generative loss and center loss, which not only minimizes the reconstructed errors of the imputed entries, but also ensures each imputed entry and its neighbors conform to the local spatio-temporal distribution. Then, the discriminator uses the convolution neural network classifier to judge whether the imputed matrix conforms to the global spatio-temporal distribution. As for the network architecture of the generator, we introduce the skip-connection to keep all well preserved data unchanged, and employ the dilated convolution to capture the spatio-temporal correlation in the traffic data. The experimental results show that o

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Author

Ye Yuan; Yong Zhang; Boyue Wang; Yuan Peng; Yongli Hu; Baocai Yin

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)