Machine learning discovery of regional and social disparities in electric vehicle charging reliability with GPT-5 (Papers Track)
Yifan Liu (Georgia Institute of Technology); Lindsey Snyder (Georgia Institute of Technology); Omar Asensio (Georgia Institute of Technology)
Abstract
There is growing interest in studying charger reliability to address persistent barriers to electric vehicle (EV) adoption and advance the decarbonization of transportation, one of the largest emitting sectors globally. Improved measurement of charger reliability is critically needed to accelerate network effects to promote EV adoption, develop pay-as-you-use infrastructure, and aggregate intelligence for more responsive service operations. However, prior methods for assessing charger reliability, which typically rely on citizen-generated data and expensive expert annotation/supervision, have proven inadequate for identifying regional and social disparities in charging performance. Prior architectures have often lacked the detection accuracy necessary for large-scale inference, especially with imbalanced datasets. This study introduces a machine learning pipeline that detects spatial disparities in charger reliability based on 838,785 U.S. consumer reviews of their experiences. We document new performance benchmarks in reliability detection using zero and few shot learning capabilities and expert counterfactual reasoning (F1 score: 0.97, SD: 0.02), outperforming previous models in the domain of electric mobility, such as ClimateBERT. To enable spatial analyses, we further demonstrate how reliability measures can be combined with popular diversity indices to inform economic and policy decision-making. Using this approach, we find evidence of widespread charging reliability issues in about half of all U.S. counties (1,653 of 3,244 counties), especially in the most populated areas. Disparities in charger reliability are most pronounced in metropolitan areas and along federally-designated EV corridors, raising concerns about inconsistent user experiences in high-traffic zones. This scalable and evidence-based approach to data discovery can be integrated into a wide range of causal inference and prediction settings in electric mobility.