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Article summary:

1. Spatio-temporal data in urban systems are becoming increasingly available and important for urban intelligent management decisions.

2. Traditional statistical learning and deep learning methods struggle to capture the complex correlations in spatio-temporal data, leading to the development of spatio-temporal graph neural networks (STGNNs).

3. Designing effective spatial dependencies learning modules, temporal dependencies learning modules, and spatio-temporal dependencies fusion methods remains a challenging problem for different predictive learning tasks using STGNNs.

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