Impact of charging infrastructure on electric vehicle adoption: A synthetic population approach

Abstract

There is limited availability of travel survey data on households with electric vehicles (EVs) and a lack of evidence on factors influencing EV ownership levels at a finer geographic level, which are crucial for optimizing public charging infrastructure investments. To address this gap, we propose an integrated approach utilizing a discrete choice model and a Bayesian network-generated synthetic population. Applied to Maryland, the model analyzes the impact of public charging stations (level-2 and DC fast chargers) on EV ownership at the census tract level. Access to fast charging, workplace charging, and the possibility of teleworking are key factors influencing EV ownership. The model, applied to the synthetic population, predicts higher EV growth in suburban regions compared to urban areas and a larger increase in EV adoption among high-income groups. This highlights potential disparities in EV adoption and demonstrates the application of this methodology in understanding micro-level EV adoption rates for informing targeted policies and infrastructure development to promote equitable adoption.