# Demand Prediction for Electric Vehicle Sharing

## TL;DR

This paper introduces a novel demand prediction method for EV sharing stations that models system dynamics using local temporal encoding and spatial correlations via graph neural networks, significantly improving prediction accuracy.

## Contribution

The paper presents a new demand prediction approach combining temporal and spatial encoding with graph neural networks for EV sharing systems.

## Key findings

- Outperforms existing demand prediction methods on real-world data.
- Effectively models both temporal dynamics and spatial correlations.
- Enhances EV station demand forecasting accuracy.

## Abstract

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.