Deep learning on graphs for mobility-flows prediction and air quality in urban area
The work targeted in this thesis is part of the current global context of reducing the energy footprint of mobility through innovative techniques. Within IFPEN, work is already being carried out on eco-driving and eco-routing, but also on mobility modeling via hybridization approaches between classical physical models and new mobility data (floating mobile data, floating car data, census data, survey data on people's mobility, telephone data, etc.).