Collectiveware: Highly-parallel algorithms for collective intelligence

The HPA4CF project aims to develop novel AI optimisation algorithms to deal with computational challenges associated with shared mobility and cooperative learning, quantify the potential benefits, and, ultimately, help policy makers to take better collective decisions.

Funding Agency: H2020-EU.1.3.2. – Nurturing excellence by means of cross-border and cross-sector mobility. MSCA-IF-2016 – Individual Fellowships. Grant agreement ID: 751608. Filippo Bistaffa (IIIA).

Computing Sustainable Policies for Online Ridesharing

With the growing popularity of the shared economy, ridesharing services are called to transform urban mobility. Indeed, shared mobility is expected to have major environmental and economic impacts by reducing pollution (e.g., CO₂ emissions and noise pollution), traffic congestion, and energy consumption. Furthermore, ridesharing is said to become even much more attractive in a future world of self-driving cars, and spur a transition from solo driving to mass transit.