CONTEXT-AWARE FOOTFALL INTELLIGENCE FOR HISTORIC DISTRICTS VIA DIGITAL TWINS

PER-SENSOR MACHINE LEARNING AND SPATIO-TEMPORAL GRAPH NETWORKS IN THE REGEN PROJECT

Authors

  • Asel Villanueva-Merino TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico y Tecnológico de Bizkaia, Edificio 700, 48160 Derio (Bizkaia), Spain. https://orcid.org/0000-0001-7304-4029
  • Jose Luis Izkara Universidad de Deusto, Unibertsitate Etorbidea 24, 48007 Bilbao (Bizkaia), Spain. https://orcid.org/0000-0001-5145-1985
  • Maria Vivar Universidad de Deusto, Unibertsitate Etorbidea 24, 48007 Bilbao (Bizkaia), Spain.
  • Silvia Urra-Uriarte TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico y Tecnológico de Bizkaia, Edificio 700, 48160 Derio (Bizkaia), Spain. https://orcid.org/0000-0001-5247-0192

DOI:

https://doi.org/10.66838/sauc.6401

Keywords:

Urban digital twin, Sustainable smart cities, Footfall prediction, Graph neural networks, Urban planning, Contextual data, Low-cost sensing

Abstract

Managing historic districts requires accurate footfall forecasting, yet they often rely on sparse, low-cost sensor networks. We present an operational footfall-intelligence architecture deployed in Laredo's historic center (Spain) using 13 sensors under the EU REGEN project. The system combines a context-enriched tabular engine (Random Forest: RMSE 5.53, R² 0.87) for interpretable single-step nowcasting with a graph neural network (T-GCN: MAE 6.17 at 1 h) to capture spatial flow redistribution and mitigate noise. Integrated into a 3D urban digital twin, this hybrid coupling of contextual machine learning with spatio-temporal graphs yields actionable intelligence for constrained heritage management.

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Published

2026-10-01

How to Cite

Villanueva-Merino, A., Izkara, J. L., Vivar, M., & Urra-Uriarte, S. (2026). CONTEXT-AWARE FOOTFALL INTELLIGENCE FOR HISTORIC DISTRICTS VIA DIGITAL TWINS: PER-SENSOR MACHINE LEARNING AND SPATIO-TEMPORAL GRAPH NETWORKS IN THE REGEN PROJECT . Street Art & Urban Creativity, 12(5), 538–558. https://doi.org/10.66838/sauc.6401

Issue

Vol. 12 No. 5 (2026)

Section

Research articles

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