AI tools for Cities

The tools are able to:

1. Build a digital replica that captures key city characteristics, including 3D models of buildings, streets, and traffic systems for a comprehensive urban representation.
2. Aggregate and integrate urban data from diverse sources - cadastral records, historical data, and real-time inputs (e.g., traffic flow) - to establish a robust foundation for simulations.
3. Harness AI-powered analytics to process urban data and run simulations tin order to test different scenarios.
4. Visualise and interpret simulation results to support data-driven decision-making.

This enables the development of optimal urban plans and strategies aligned with green initiatives, energy efficiency and sustainable policies.

Data collection

Sensors capture traffic flow, vehicle types, emissions, and air quality.

Data analytics

The gathered urban data is analysed to reveal trends and patterns.

Scenario simulation

Various scenarios for implementing the envisaged plans or strategies for a low emission zone are simulated.

Impact visualisation

The results and impact per scenario can be visualised enabling an informed review and stakeholder engagement before any policy implementation.

Citizen engagement

Collect resident feedback on the plans, e.g. with extended or Virtual Reality tools.

Decision making and evaluation

Use visuals, and feedback for informed, confident decisions as well as feedback on the outcome of decisions.

Mobility planning

Traffic flow, public transport, and accessibility.

Energy optimisation

Greenhouse gas emissions, fluctuating energy demands on grids, energy-efficient housing.

Urban health

Pollution spread and impact, high temperature mitigation.

Urban planning

Buildings renovation; mitigation actions for increasing population density, identifying inefficient land use or inadequate housing.