# TD-STGT: A Spatio-Temporal Graph Transformer for Mobile Traffic Demand Forecasting

arXiv: https://arxiv.org/abs/2609.06636 · HTML: https://mlsanity.com/paper/2609.06636/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 6 Sep 2026.

## Builds on (most cited first, 4 of 4)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction](https://mlsanity.com/paper/2303.00498/) (2023)
- [Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand](https://mlsanity.com/paper/2603.09942/) (2026)
- [A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks](https://mlsanity.com/paper/2603.09859/) (2026)

## Abstract (arXiv)

Fine-grained mobile traffic demand forecasting is essential for long-term planning of 5G and future 6G networks, including radio upgrades, site densification, backhaul expansion, and spectrum activation. This paper proposes the Traffic Demand Spatio-Temporal Graph Transformer (TD-STGT), a graph neural forecasting framework for predicting changes in wireless mobile traffic demand across fine geographic grids. The framework uses a population-scaled demand proxy developed from crowdsourced mobile measurements and daytime population information. Experiments across five Canadian metropolitan regions show that TD-STGT achieves the best performance in forecasting grid-level demand changes, reaching a Delta R2 of 0.462 and reducing DeltaRMSE by 5.7% relative to the strongest baseline. The proposed model provides a practical tool for identifying areas with increasing demand pressure and prioritizing future mobile-network capacity upgrades.