Transform Urban Planning with EU LDT Toolbox and AI-Powered Insights
Cities across Europe are embracing Local Digital Twins (LDTs) to simulate, predict, and plan sustainable urban futures. The EU LDT Toolbox provides a modular, open-source framework that empowers municipalities to create data-driven strategies for mobility, energy, health, and climate resilience adaptable to each city’s specific needs, priorities, and creativity.
This edition explores real-world use cases and dives into the AI models powering these solutions.
Featured Use Cases
Low Emission Zones (LEZ)
Simulate and visualise the impact of LEZ strategies before implementation.
The Toolbox enables cities to:
- Model traffic and pollution scenarios
- Generate synthetic data when real-time inputs are missing
- Engage citizens through participatory planning tools
Impact: Early pilots show up to 18% reduction in PM2.5, 25% less congestion, and 35% increase in zero-emission vehicle use.
Energy Optimization
Forecast neighbourhood energy demand and design low-emission zones using predictive models.
- AI-driven forecasting with LSTM and ARIMA models
- Integration of renewable energy data for sustainability planning
Urban Health & Pollution Control
Leverage advanced simulation tools to monitor and mitigate air quality issues.
- CHIMERE and WRF models estimate pollutant dispersion
- Strategic placement of green zones and climate shelters
AI Models at the Core
The Toolbox provides the capabilities to integrate ready-to-use algorithms and supports custom AI model development via the AI Notebook, where cities and developers can build, train, and adapt models using local or external data. The Marketplace acts as a distribution and discovery layer to publish, share, and reuse AI models and components developed by cities or third parties, but does not perform model development itself. Key model categories include:
- Building Environmental Footprint Model: Assigns energy efficiency labels and predicts CO₂ emissions
- Neighbourhood Energy Demand Forecasting: Uses LSTM for time-series predictions
- Urban Mobility Simulation: SUMO-based traffic and emissions modelling
- Pollution Propagation: Regional-scale pollutant concentration mapping
Cities can train models on local data or apply federated learning for secure collaboration across municipalities.
Why it matters
By combining data acquisition, AI-driven modelling, and scenario simulation, the EU LDT Toolbox helps cities make informed decisions that improve air quality, reduce energy waste, and enhance quality of life.