Examining Spatial Disparities in Electric Vehicle Charging Station Placements Using Machine Learning

Electric vehicles (EV) are an emerging mode of transportation that has the potential to reshape the transportation sector by significantly reducing carbon emissions thereby promoting a cleaner environment and pushing the boundaries of climate progress. A deeper understanding of the underlying complex interactions of socioeconomic factors which may lead to such emerging disparities in EVCS access among people of all ages and abilities. In this study, we develop a machine learning framework to examine spatial disparities in EVCS placements by using a predictive approach and use decision analysis to generate an equity indicator to compare placement coverage in Orange County, California. Our method achieved the highest predictive accuracy (94.9%) of EVCS placement density at a spatial resolution of 3 km using Random Forests. with a total of 74.18% of predicted EVCS placements within areas of high spatial inequality– and 50.32% with a medium density of future EVCS placements. Our framework is generalizable and can help policymakers to identify underserved communities to facilitate targeted infrastructure investments for widespread EV usage and adoption for all