Search Results for author: Raffaele Bruno

Found 7 papers, 0 papers with code

A Machine-Learned Ranking Algorithm for Dynamic and Personalised Car Pooling Services

no code implementations6 Jul 2023 Mattia Giovanni Campana, Franca Delmastro, Raffaele Bruno

Car pooling is expected to significantly help in reducing traffic congestion and pollution in cities by enabling drivers to share their cars with travellers with similar itineraries and time schedules.

Learning-To-Rank Recommendation Systems

Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning

no code implementations1 Oct 2021 Lorenzo Valerio, Raffaele Bruno, Andrea Passarella

We show that our system based on Reinforcement Learning is able to automatically learn a very efficient strategy to reduce the traffic on the cellular network, without relying on any additional context information about the opportunistic network.

Q-Learning reinforcement-learning +1

Weak Signals in the Mobility Landscape: Car Sharing in Ten European Cities

no code implementations20 Sep 2021 Chiara Boldrini, Raffaele Bruno, Haitam Laarabi

Car sharing is one the pillars of a smart transportation infrastructure, as it is expected to reduce traffic congestion, parking demands and pollution in our cities.

Vaccination and SARS-CoV-2 variants: how much containment is still needed? A quantitative assessment

no code implementations17 Feb 2021 Giulia Giordano, Marta Colaneri, Alessandro Di Filippo, Franco Blanchini, Paolo Bolzern, Giuseppe De Nicolao, Paolo Sacchi, Raffaele Bruno, Patrizio Colaneri

Despite the progress in medical care, combined population-wide interventions (such as physical distancing, testing and contact tracing) are still crucial to manage the SARS-CoV-2 pandemic, aggravated by the emergence of new highly transmissible variants.

Car sharing through the data analysis lens

no code implementations25 Jul 2017 Chiara Boldrini, Raffaele Bruno, Haitam Laarabi

Car sharing is one the pillars of a smart transportation infrastructure, as it is expected to reduce traffic congestion, parking demands and pollution in our cities.

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