In your project " Platform Data Fusion Generator - DaFne ," how does synthetic data generation contribute to smart cities - and how is AI involved in this process ? In the DaFne project , we defined ten use cases , movement in urban districts , tourist transfer to mobility centres , urban civil protection , hazard prediction at major events , bridge maintenance , preventive management of air pollution , social dynamics at places of knowledge and innovation , light and mobility , watering for the climate green space irrigation , city signals statistical methods or semantic CMS .
Our focus , is on mobility , and maintenance .
Regarding mobility we are currently researching two different methods : 1 . Neighbourhood generation , and 2 . Path generation
These methods utilise deep reinforcement learning to explore pedestrian mobility habits within cities .
The maintenance approach involves tabular data generation , and we are testing it using bridge datasets . We have developed a path generator tool called " motivity ," which uses deep reinforcement learning and connects with a survey to generate happier or unhappier paths for citizens .
The AI plays the game according to predefined rules set by scientists , learning
18 August 2023