Abstract
With more and more advanced sensing and communication devices deployed on vehicles, ubiquitous vehicle location data (e.g., GPS) are available, which provide us great opportunities to enhance their mobility and energy performance (e.g., better scheduling decisions for operation or charging of taxis). In this paper, we design a real-time charging scheduling system called tCharge to address the charging problem of electric taxis with a fleet-oriented fashion. We leverage historical GPS data to estimate the travel time of electric taxis to different charging stations, and we utilize real-time GPS data of electric taxis to infer their waiting times at different charging stations. Then all these obtained information is fed to an online optimization for a fleet-oriented scheduling decision. We implement tCharge with real-world data from the Chinese city Shenzhen, including GPS data, taxi transaction data, road network data, and charging station data from more than 1,000 electric taxis and 28 charging stations. The results show our tCharge outperforms existing methods by 82% of the queuing time reduction.
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