The field of urban forecasting is moving towards the development of more sophisticated spatio-temporal graph neural networks (GNNs) that can effectively capture complex dependencies in urban systems. These models are being applied to various domains, including traffic flow forecasting, urban traffic risk forecasting, weather forecasting, and crime prediction. The integration of GNNs with other architectures, such as Transformers, and the incorporation of external features and knowledge graphs are leading to significant improvements in predictive accuracy. Noteworthy papers in this area include:

• A Cloud-Based Spatio-Temporal GNN-Transformer Hybrid Model for Traffic Flow Forecasting, which proposes a scalable and real-time adaptable model for traffic flow forecasting.
• MDAS-GNN: Multi-Dimensional Spatiotemporal GNN with Spatial Diffusion for Urban Traffic Risk Forecasting, which develops a framework for urban traffic risk forecasting that captures complex spatial relationships and temporal dependencies.
• Adaptive Spatio-Temporal Graphs with Self-Supervised Pretraining for Multi-Horizon Weather Forecasting, which proposes a novel self-supervised learning framework for multi-variable weather prediction.
• Learning A Universal Crime Predictor with Knowledge-guided Hypernetworks, which proposes a framework for predicting crimes in urban environments that can effectively train a unified predictor without assuming identical crime types in different cities' records.
• Using ensemble learning with hybrid graph neural networks and transformers to predict traffic in cities, which presents a hybrid architecture that integrates GNNs, Transformers, and supervised ensemble learning methods for traffic prediction.