AI-Driven Urban Data Analytics

🖥️ Platform

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🧠 Data
🇪🇸 Spain

Universidade da Coruña

Universidade da Coruña (UDC) is a public Spanish university at the forefront of digital transformation and sustainable urban development. Through specialized research centers like CITIC (ICT) and CITEEC (Civil Engineering), UDC develops advanced AI, big data, and smart infrastructure solutions. The university excels in translating academic research into practical applications for intelligent transportation, sustainable construction, and efficient resource management, fostering resilient and data-driven urban ecosystems.

Context and problem statement

Modern cities generate petabytes of data from sensors, mobile devices, and cameras, yet this information often remains in “silos.” Without an integrated analytical layer, city planners cannot see the correlation between different urban systems (e.g., how weather patterns affect traffic flow or how social events impact energy demand). There is a critical need for a platform that can synthesize this “Big Data” into actionable intelligence.

Solution overview

Specific problems / challenges addressed:

  • Information fragmentation across different city departments.
  • Inability to predict peak demands in public services.
  • High operational costs due to inefficient resource allocation.

Value proposition:

The platform enables “Evidence-Based Urban Governance.” By transforming raw data into visual indicators and predictive models, it allows cities to optimize public transport, reduce energy waste, and respond faster to urban emergencies, ultimately improving the quality of life for citizens.

Functional Scope and Features

  • Key functionalities: Real-time data ingestion, predictive modeling (ML/DL), anomaly detection, and interactive dashboarding.
  • Type of data used: Traffic cameras (vision data), IoT weather sensors, public transport GPS feeds, and energy meter data.
  • Outputs produced: Heatmaps of urban activity, predictive traffic alerts, energy saving recommendations, and environmental reports.
  • Innovation: The platform uses Edge Computing techniques to process data closer to the source (e.g., on-camera), reducing latency and bandwidth requirements for the city’s network.

Use Cases and Deployments

  • Main use cases: Traffic and mobility management, environmental monitoring (air quality/noise), and smart energy grids.
  • Deployment examples: Tested in collaboration with the A Coruña City Council as part of the “Coruña Smart City” initiative.
  • Scale of application: City and Metropolitan levels.

Related Challenges

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Related Pilots

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Interested in this solution?

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