Rethinking Embedding Vectors for Electric Vehicle Charging Stations: An Empirical Study

Electric vehicle (EV) charging stations are critical in promoting EV adoption and mitigating global warming by reducing reliance on fossil fuels. However, a comprehensive understanding of the latent characteristics of these charging stations remains limited. Unveiling these latent features is essential for enhancing predictive tasks such as utilization prediction, demand forecasting, and strategic infrastructure planning. In this paper, we conduct a comprehensive investigation into methods for extracting embedding vectors of charging stations based on userstation interactions. We explore a spectrum of techniques—from traditional approaches like non-negative matrix factorization to advanced machine learning models such as neural collaborative filtering—to effectively capture these latent features. Through extensive experiments and analyses, we evaluate the quality and effectiveness of the generated embeddings in improving predictive modeling tasks related to charging station usage. Our findings demonstrate that incorporating these embeddings significantly enhances the performance of predictive models, leading to more accurate demand forecasts and better utilization predictions. To the best of our knowledge, this is the first study to delve deeply into extracting and analyzing embedding vectors of charging stations derived from user interaction data. The insights gained from this research provide valuable guidance for optimizing EV charging infrastructure and can inform future developments in the field, ultimately supporting the broader adoption of electric vehicles.