- 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.
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- Project code: 2022XJ9SX
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