Abstract
A comprehensive understanding of charging behaviors among electric vehicle users is crucial for advancing green transportation and deploying effective charging infrastructure. This study conducted large-scale empirical research using data from electric taxi fleets in Shenzhen to explore the heterogeneity of charging behaviors among taxi drivers. The study hypothesized that distinct charging patterns exist within the electric taxi fleet, impacting fleet-wide fluctuations and repetitions of charging and service activities. Employing a covariate-enhanced latent profile analysis model, we examined unique charging patterns within the fleet and investigated relationships between taxi service attributes and charging behavior heterogeneity. Fleet-wide diurnal fluctuations, daily repetitions, and subgroup-specific charging patterns were identified. At the micro level, operational activity sequence similarity and between-group diversity were assessed. The findings offer valuable insights for policymakers and stakeholders involved in promoting green transportation and optimizing charging infrastructure.
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