Understanding charging accessibility for electric vehicles (EVs) is crucial for supporting EV adoption and ensuring efficient infrastructure deployment. However, existing studies often overlook dynamic variations in accessibility due to traffic conditions, station availability, and network demand fluctuations. This study addresses these gaps by introducing a framework for evaluating multi-tier EV charging station accessibility, incorporating both spatial and temporal variations. Using origin–destination travel data at a high spatial resolution, accessibility patterns are quantified and their implications are assessed for optimal station deployment. The analysis leverages Gaussian two-step floating catchment area methods and real-time travel data to capture dynamic accessibility fluctuations. To evaluate the model’s effectiveness, this study applies the proposed framework to the state of Georgia, leveraging its diverse transportation and infrastructure characteristics. Key findings indicate that significant temporal fluctuations in charging accessibility are influenced by peak traffic periods and station availability, thus affecting user convenience and network efficiency. Additionally, an optimization model is developed to determine the ideal number and spatial distribution of charging stations, balancing user demand and infrastructure investment. The results provide insights for policymakers and planners to improve EV charging accessibility while optimizing station placement for enhanced operational efficiency.
