Building a continuously updated digital twin that fuses sensor, mobility, and environmental data to let planners test interventions before they touch the street.
Overview
A digital twin is only useful if it is honest about uncertainty. This project builds a living model of a mid-sized city that planners can interrogate — “what happens to air quality if we pedestrianise this street?” — with calibrated confidence bounds rather than false precision.
Pillars
- Data fusion. Mobility, sensing, and environmental streams reconciled in near real time.
- Scenario testing. A sandbox for interventions, with explicit uncertainty.
- Governance. Clear provenance for every layer feeding a decision.
Status
Phase one — data infrastructure and provenance tracking — is underway, with the first planner-facing scenario tool due in 2026.
Collaborators on this project
PS
Prof. Sarah Chen
Massachusetts Institute of Technology
DP
Dr. Priya Sharma
ETH Zürich
DA
Dr. Aoife Murphy
University College Dublin