E2M: Evolving Mobility Modeling in Metropolitan-Scale Electric Taxi Systems

Human mobility data play an important role in addressing various urban issues. However, when a new mobility paradigm emerges and continuously evolves with time, it is usually hard to obtain a large-scale and evolving mobility dataset due to various factors such as social and privacy concerns. In this paper, we focus on modeling the evolving mobility of metropolitan-scale electric taxis (ETs), which have different mobility patterns with petroleum vehicles and continuously evolve with the expansion of the ET fleet and the charging station network. To this end, the E2M system is proposed to generate trajectories for large-scale ET fleets by learning the mobility from only a small-scale ET fleet and the corresponding charging station network. First, the ET mobility is decomposed and modeled with transition, charging, and resting patterns. Second, the E2M system generates trajectories with a fleet generation algorithm. Extensive experiments are conducted on a real-world dataset, which has ET trajectories during both the early stage and mature stage in the taxi electrification process in Shenzhen, China, and the results verify the effectiveness of E2M.