Public bike-sharing systems are an emerging mode of transportation introduced by municipalities to solve congestion problems in metropolitan areas, especially when integrated with other types of transportation. In the last years, the number of public bike sharing services is constantly on the rise all over the world, and generally the overall satisfaction with them is high. However, satisfaction with public services is driven by mechanisms that can differ from those in the private sector. It is important to establish to what extent a high satisfaction is genuine or simply ephemeral. Even "old" public services (like public transportation) become "gold" when accompanied by the introduction of new technologies. In this paper we analyse this phenomenon using data from a satisfaction web-survey conducted among customers of the public bike sharing system 'BikeMi' in Milan, Italy, in a period when mobile technologies have been introduced to speed up the service. On simply analysing the responses to satisfaction questions, satisfaction resulted very high. However, our aim was to look for potential 'darker' sides of the service by

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In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.