Fairness-Aware Electric Taxi Fleet Coordination Under Short-Term Power System Failures

Yukun Yuan, Zihan Ding, Shan Lin

Abstract: Power outages and shortages, among other disruptive events, can markedly diminish the charging efficiency of EV fleets, e.g., e-taxis, and compromise their service quality. This paper aims to address the challenge of coordinating e-taxis amidst such power system disruptions. To understand the extent of the problem, we employ a trace-driven simulation to measure how short-term power failures influence e-taxi service quality. Our observations highlight drops in passenger service in affected areas, pointing to a potential disparity in service quality across various regions of a city. In response, we introduce the Fairness-Aware e-taxi fleet Coordination (FAC) algorithm. FAC monitors the power system disruptions and dynamically switches between two control strategies: one that optimizes city-wide performance during normal operations, and another that emphasizes both service fairness and system performance during power disruptions. We put FAC to the trace-driven evaluation using a comprehensive dataset from an existing e-taxi ecosystem, comprising almost 8,000 taxis and averaging 62,100 taxi trips daily. Our data-driven evaluation shows the effectiveness of our solution in terms of providing fair service quality across regions and enhancing the service quality in the affected regions and a city.