Electrifying taxi fleets represents a significant step towards a sustainable urban transportation system. However, there is a current gap in our comprehensive understanding of the day-to-day operations of electric taxi services. In this study, we utilize operational data from electric taxi fleets in Shenzhen, employing a two-stage approach to analyze the daily charging, cruising, and serving activities of drivers. Initially, we use latent profile analysis to thoroughly investigate the diverse operating patterns of 14,660 taxis. This analysis categorizes the taxis into six distinct subgroups, highlighting notable differences in aspects like charging demand, operational durations, and spatio-temporal distributions. Building on these subgroups, we further employ an inverse reinforcement learning framework to uncover various decision-making preferences across operating patterns, derived from the operational data. This in-depth analysis reveals the diverse spatio-temporal preferences of the subgroups, particularly in relation to range anxiety, charging, and cruising behaviors.

中文翻译：

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从宏观模式到微观行为了解电动出租车的日常运营

出租车队电气化是迈向可持续城市交通系统的重要一步。然而，目前我们对电动出租车服务日常运营的全面了解还存在差距。在本研究中，我们利用深圳电动出租车车队的运营数据，采用两阶段方法来分​​析驾驶员的日常充电、巡航和服务活动。最初，我们使用潜在概况分析来彻底调查 14,660 辆出租车的不同运营模式。该分析将出租车分为六个不同的子组，突出了充电需求、运营持续时间和时空分布等方面的显着差异。在这些子组的基础上，我们进一步采用逆强化学习框架来揭示从运营数据中得出的跨运营模式的各种决策偏好。这项深入的分析揭示了亚组不同的时空偏好，特别是与里程焦虑、充电和巡航行为相关的偏好。