Demand Prediction for Electric Vehicle Sharing
This work addresses a critical operational challenge for EV sharing service providers and manufacturers, enabling better fleet management and market competitiveness, though it appears incremental as it builds on existing neural network techniques.
The paper tackles the problem of dynamically predicting demand for electric vehicle sharing stations by proposing a novel approach that models system dynamics using local temporal encoding and spatial encoding with graph convolutional neural networks, and it demonstrates significant outperformance over state-of-the-art methods on real-world data.
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.