Project Summary
There are significant hurdles to the widespread adoption of electric vehicles (EVs) in the United States ranging from the high cost of EVs to the inequitable placement of EV charging stations (EVCS). A deeper understanding of the underlying complex interactions of social, economic, and demographic factors which may lead to such emerging disparities in EVCS placements is necessary to mitigate accessibility issues and improve EV usage among people of all ages and abilities. This project developed a machine learning framework to examine spatial disparities in EVCS placements using a predictive approach. As a first step, the research team identified the socioeconomic factors that may contribute to spatial disparities in EVCS access. Second, they used these factors along with ground truth data from existing EVCS placements to predict future ECVS density at multiple spatial scales using machine learning algorithms and compared their predictive accuracy to identify the most optimal spatial resolution for the predictions. Finally, the research team compared the most accurately predicted EVCS placement density with a spatial inequity indicator to quantify how equitably these placements would be for Orange County, California. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all.
