부산대학교 도서관

Mobility will surely be at the core of the smart cities of the future. As such, it must be planned based on novel mobility models, smart enough to answer the multifaceted needs of users, while being sustainable and energy efficient. In this evolution, electric vehicles (EVs) will be crucial, as confirmed by the fact that many governments are already actively sustaining their spread in place of common internal combustion engine (ICE) ones. Nonetheless, for their adoption to be actually widespread, one must be able to govern the mass adoption mechanisms, by designing policies that are cost-effective and successful in making the mobility transition a reality in due time. In this work, we propose a novel framework that can represent a valuable control-oriented tool to serve this ambitious goal. Our framework lays its foundation on a quantitative description of the inclination of traditional car owners toward EVs, which is retrieved by relying on data-driven insights on their mobility habits only. This information is further exploited to construct a proximity-based network, that is combined with the individual characterization into a cascade model describing the adoption dynamics. To show the potential of the introduced framework, we exploit it to assess the unforced spread of EVs starting from a set of known EV owners, and to test and quantitatively evaluate the cost and benefits of policies enacted to foster adoption.