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Title	Spatio-Temporal Graph Neural Networks For Missing Data Completion In Traffic Prediction
ID_Doc	52566
Authors	Chen J.; Yang L.; Yang Y.; Peng L.; Ge X.
Year	2025
Published	International Journal of Geographical Information Science, 39, 5
DOI	http://dx.doi.org/10.1080/13658816.2024.2381221
Abstract	Missing traffic data completion is a key part of the construction of a smart city. However, due to cost constraints and other reasons, many locations do not have sensors to record traffic data. Most research methods do not systematically consider filling in missing traffic data. This study explores a new spatio-temporal feature extraction layer that includes spatio-temporal feature fusion, graph learning on an adaptive adjacency matrix, and a gated recurrent unit with a mask for missing traffic data completion. This idea is based on a hypothesis: missing data can be inferred from the spatio-temporal features of other nearby recorded sensor nodes. Therefore, we propose an end-to-end traffic model dealing with missing data - missing traffic data completion graph neural networks (MTC-GNN). Experiments demonstrate that the proposed model can learn spatio-temporal patterns and fill in speed from traffic data with various missing ratios and outperform existing models. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
Author Keywords	data completion; graph neural networks; Missing traffic data

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