  This paper is devoted to proposing a data-driven approach for electrifying
the urban taxi fleet. Specifically, based on the gathered real-time vehicle
trajectory data of 39053 taxis in Beijing, we conduct time-series simulations
to derive insights on both the configuration of electric taxi fleet and
dispatching strategies. The proposed simulation framework accurately models the
electric vehicle charging behavior from the aspects of time window, charging
demand and availability of unoccupied charges, and further incorporates a
centralized and intelligent fleet dispatching platform, which is capable of
handling taxi service requests and arranging electric taxis' recharging in real
time. To address the impacts of the limited driving range and long
battery-recharging time on the electrified fleet's operations efficiency, the
dispatching platform integrates the information of customers, taxi drivers and
charging stations, and adopts a rule-based approach to achieve multiple
objectives, including reducing taxi customers' average waiting time, increasing
the taxi demand fill rate and guaranteeing the equity of the income among taxi
drivers. Although this study only examines one type of fleet in a specific
city, the methodological framework is readily applicable to other cities and
types of fleet with similar dataset available.
