Scene-Conditioned Relation Routing for urban cellular activity forecasting

Abstract:Urban cellular activity forecasting requires jointly modeling heterogeneous spatiotemporal signals, including SMS usage, mobile network traffic, and call activity. Existing methods often separate temporal modeling, spatial relation learning, and multi-signal prediction, relying on fixed graph structures or static multi-task learning schemes, which limits their adaptability to changing urban scenes. We propose SCRR-Net, a scene-conditioned spatial relation routing framework in which urban contextual information jointly controls spatial dependency selection and cross-task knowledge transfer. SCRR-Net includes a context encoder, a spatial graph expert routing module, a temporal Transformer encoder, and a task knowledge routing module. Experiments on the Milano and Trento datasets demonstrate that SCRR-Net consistently outperforms competing methods on SMS, network traffic, and call activity forecasting, while providing interpretable routing behaviors.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.20209 [cs.LG]

(or arXiv:2609.20209v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.20209 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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