Study of emergent collective phenomena at metropolitan level in personal navigation assistance systems with different recommendation policies, with respect to different collective optimization criteria (fluidity of traffic, safety risks, environmental sustainability, urban segregation, response to emergencies, …).
Idea: (1) start from real big mobility data (massive datasets of GPS trajectories at metropolitan level from onboard black-boxes, recorded for insurance purposes), (2) identify major road blocks events (accidents, extraordinary events, …) in data, (3) simulate the effect (modify the data) that users involved in a road block were previously supported by navigation systems the employ policies to mitigate the impact of the block, by using different policies different from individual optimization, aiming at collective optimization (aiming at diversity, randomization, safety, resilience, etc.)
Compare the impact of the different choices in term of aggregated impact.
(Big-) data-driven simulations with scenario assessment
- Università di Pisa (UNIPI), Dino Pedreschi
- Consiglio Nazionale delle Ricerche (CNR), Mirco Nanni
- German Research Centre for Artificial Intelligence (DFKI), Paul Lukowicz
Primary Contact: Mirco Nanni, CNR