Go to DBLP homepageFairMove: A Data-Driven Vehicle Displacement System for Jointly Optimizing Profit Efficiency and Fairness of Electric For-Hire Vehicles
Abstract: With the worldwide mobility electrification initiative to reduce air pollution and energy security, more and more for-hire vehicles are being replaced with electric ones. A key difference between gas for-hire vehicles and electric for-hire vehicles (EFHV) is their energy replenishment mechanisms, i.e., refueling or charging, which is reflected in two aspects: (i) much longer charging processes versus much shorter refueling processes and (ii) time-varying electricity prices versus time-invariant gasoline prices during a day. The complicated charging issues (e.g., long charging time and dynamic charging pricing) potentially reduce the daily operation time and profits of EFHVs, 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 and field studies, in this paper, we design a fairness-aware vehicle displacement system called FairMove to jointly optimize the overall profit efficiency and profit fairness of EFHV drivers by considering both the passenger travel demand and vehicle charging demand. We first formulate the EFHV displacement problem as a Markov decision problem, and then we present a fairness-aware 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 over 20,100 EFHVs, coupled with the data of 123 charging stations, which constitute, to our knowledge, the largest EFHV network in the world. Extensive experimental results show that our fairness-aware FairMove effectively improves the profit efficiency and profit fairness of the EFHV fleet by 26.9% and 54.8%, respectively. It also improves the charging station utilization fairness by 38.4%.
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