Building a Digital Twin Validated City Program
Planning validation is realized in four steps:
- parameter mapping stage: Convert real city indicators (population density/traffic flow) into intelligent body behavioral parameters, e.g., set the "commuting tendency" parameter to correspond to the actual travel data.
- Policy testing phase: Implementation of proposed policies (e.g., single- and double-numbering restrictions) in a sandbox and observation of derived impacts (changes in business distribution/fluctuations in land prices) through the adaptive behavior of the intelligentsia
- stress test: Inject unexpected events (subway shutdowns/extreme weather) with a "disaster simulator" to document the system's self-regulation process.
- Data export: Export of simulation results (social resilience index/resource redistribution efficiency) to traditional analytical tools using the platform's API interface
Case reference: a pilot city had passed a setDriverless car penetrationparameters and found that intersection priority rules need to be reconstructed when the penetration rate is >40%, a finding that warns of potential problems six months earlier than traditional traffic models.
This answer comes from the articleAivilization: a social simulation sandbox exploring human-AI coexistenceThe
May not be reproduced without permission:AI productivity tools " How can Aivilization's simulation capabilities be applied in urban planning scenarios?
