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.

中文翻译：

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电动出租车充电行为的异质性：与出行服务特征的关联


全面了解电动汽车用户的充电行为对于推进绿色交通和部署有效的充电基础设施至关重要。本研究利用深圳电动出租车车队的数据进行了大规模的实证研究，以探讨出租车司机充电行为的异质性。该研究假设电动出租车车队中存在不同的充电模式，影响整个车队的波动以及充电和服务活动的重复。采用协变量增强的潜在概况分析模型，我们检查了车队内独特的充电模式，并研究了出租车服务属性与充电行为异质性之间的关系。确定了车队范围的昼夜波动、每日重复和特定于子组的收费模式。在微观层面，评估了操作活动序列相似性和组间多样性。这些发现为参与促进绿色交通和优化充电基础设施的政策制定者和利益相关者提供了宝贵的见解。