  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. From the point of view of demand modelling, car sharing is a weak
signal in the city landscape: only a small percentage of the population uses
it, and thus it is difficult to study reliably with traditional techniques such
as households travel diaries. In this work, we depart from these traditional
approaches and we leverage web-based, digital records about vehicle
availability in 10 European cities for one of the major active car sharing
operators. We discuss which sociodemographic and urban activity indicators are
associated with variations in car sharing demand, which forecasting approach
(among the most popular in the related literature) is better suited to predict
pickup and drop-off events, and how the spatio-temporal information about
vehicle availability can be used to infer how different zones in a city are
used by customers. We conclude the paper by presenting a direct application of
the analysis of the dataset, aimed at identifying where to locate maintenance
facilities within the car sharing operation area.
