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

• URL: http://arxiv.org/abs/2609.06636v1
• Date: Sun, 06 Sep 2026 14:33:19 GMT
• Title: TD-STGT: A Spatio-Temporal Graph Transformer for Mobile Traffic Demand Forecasting
• Abstract summary: This paper proposes a graph neural forecasting framework for predicting changes in wireless mobile traffic demand across fine geographic grids.<n>The framework uses a population-scaled demand proxy developed from crowdsourced mobile measurements and daytime population information.
• Score: 19.56599244753046
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 $ΔR^2$ of 0.462 and reducing $Δ$RMSE 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.

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