Data-Driven Fairness-Aware Vehicle Displacement for Large-Scale Electric Taxi Fleets

We are witnessing a rapid taxi electrification process due to the ever-increasing concern about urban air quality and energy security. A key difference between conventional gas taxis and electric taxis is their energy replenishment mechanisms, i.e., refueling or charging, which is reflected in two aspects: (i) much longer charging processes vs. short refueling processes and (ii) time-varying electricity prices vs. time-invariant gasoline prices during a day. The complicated charging issues (e.g., long charging time and dynamic charging pricing) potentially reduce electric taxis' daily operation time and profits, and also cause overcrowded charging stations during some off-peak charging pricing periods. Motivated by a set of findings obtained from a data-driven investigation, in this paper, we design a fairness-aware vehicle displacement system called FairMove to improve the overall profit efficiency and profit fairness of electric taxi fleets by considering both the passenger travel demand and taxi charging demand. We first formulate the electric taxi displacement problem as multi-agent deep reinforcement learning, and then we propose a centralized multi-agent actor-critic approach to tackle this problem. More importantly, we implement and evaluate FairMove with real-world streaming data from the Chinese city Shenzhen, including GPS data and transaction data from more than 20,100 electric taxis, coupled with the data of 123 charging stations, which constitute, to our knowledge, the largest all-electric taxi network in the world. The extensive experimental results show that our fairness-aware FairMove effectively improves the profit efficiency and profit fairness of the Shenzhen electric taxi fleet by 25.2% and 54.7%, respectively.