Survival analysis of electric vehicle charging behavior and the temporal evolution of feature effects.

ABSTRACT: This study proposes a survival-based modeling framework that combines behavioral features with interpretable machine learning to understand and predict user churn in electric vehicle charging services. Using a dataset of 1,074 users and 107,531 charging sessions from Central European countries, we modeled time-to-churn while handling censored observations. The best-performing model, a Stacked Weibull survival model based on gradient boosting, achieved a concordance index of 0.826 ± 0.041 and Integrated Brier Score of 0.078 ± 0.008 (5-fold cross-validation), with strong calibration relative to Kaplan-Meier survival estimates. Interpretability analyses identified sustained session frequency, positive engagement trends, and temporal regularity in charging behavior as key predictors of reduced

SUBMITTER: Meza M 

PROVIDER: S-EPMC12504540 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature