  Mobility is undergoing dramatic transformations. Especially in the context of
urban areas, several significant changes are underway, driven by both new
mobility needs and environmental concerns. The most mature one, which still is
struggling to affirm itself is the process of the adoption of Electric Vehicles
(EVs), thus switching from fuel-based to battery-powered propulsion
technologies. Many social and economic barriers have proved to play a crucial
role in this process, ranging from level of education, environmental awareness,
age and census. This work aims at contributing to the study of this adoption
process through a data-based lens, using real mobility patterns to setup a
social-network analysis to model the spread of consensus among neighbouring
people that can enable the switch to EVs. In particular, we build the network
topology using proximity measures that emerge from the analysis of real trips,
and the initial disposition of the single agents towards the EV technology is
inferred from their real mobility patterns. Based on this network, a cascade
adoption model is simulated to investigate the dynamics of the adoption
process, and an incentive scheme is designed to show how different policies can
contribute to the opinion diffusion over time on the network.
