Heterogeneity on the Road: Modeling, Analysis, Control

The impact of road traffic on our modern world cannot be underestimated, due to its deep environmental, societal and technological implications.
In the wide effort to achieve climate neutrality of the transport sector, we aim to contribute to a more efficient management of the existing road infrastructures. We want to improve our mathematical understanding of road traffic, thus being able to provide more efficient control strategies to achieve quantifiable goals: pollution reduction, flow optimization, smarter management.
These goals can be achieved by taking into account one of the key features in road traffic: eterogeneity of the vehicles and/or the road. In particular, we aim to develop a whole set of results for four classes of models, that are paradigm of possible heterogeneities:
Multi-class models, in which two or more classes of vehicles interact.
Platoon models, in which vehicles are grouped into platoons by means of controlled smart vehicles.
Discontinuous-flux or junction models, in which the heterogeneity is the space variation of the flux, due to lane restrictions or junctions.
Congestion models, in which we focus on forecasting and mitigating congestion, with phase-transition and data-driven models.
Heterogeneities have often been overlooked in modeling, due to the intrinsic difficulties they introduce. Our aim is then to provide new mathematical tools to model, analyze and control road traffic in presence of heterogeneities. For each of the classes of models given above we aim to:
- improve the model, both by giving it solid mathematical foundations and by applying data-driven discovering methods;
- analyze its mathematical properties, to understand its suitability for real-world modeling and use it for traffic forecast;
- provide efficient control strategies, being both near-optimal for quantifiable goals (such as pollution reduction) and implementable with modern technologies. 
Project code: 2022XJ9SX