Social network analysis of electric vehicles adoption: a data-based approach

TL;DR

This paper uses real mobility data to model social network influences on electric vehicle adoption, analyzing how proximity and opinions spread to inform policy incentives.

Contribution

It introduces a data-driven social network analysis approach to model EV adoption dynamics using real mobility patterns and simulates cascade adoption processes.

Findings

Proximity-based social networks influence EV adoption.

Simulation of cascade models reveals key factors in opinion spread.

Policy incentives can accelerate adoption through targeted influence.

Abstract

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.