  Electric Vehicle (EV) sharing systems have recently experienced unprecedented
growth across the globe. Many car sharing service providers as well as
automobile manufacturers are entering this competition by expanding both their
EV fleets and renting/returning station networks, aiming to seize a share of
the market and bring car sharing to the zero emissions level. During their fast
expansion, one fundamental determinant for success is the capability of
dynamically predicting the demand of stations. In this paper we propose a novel
demand prediction approach, which is able to model the dynamics of the system
and predict demand accordingly. We use a local temporal encoding process to
handle the available historical data at individual stations, and a spatial
encoding process to take correlations between stations into account with graph
convolutional neural networks. The encoded features are fed to a prediction
network, which forecasts both the long-term expected demand of the stations. We
evaluate the proposed approach on real-world data collected from a major EV
sharing platform. Experimental results demonstrate that our approach
significantly outperforms the state of the art.
