Luo, Man, Du, Bowen, Klemmer, Konstantin, Zhu, Hongming, Ferhatosmanoglu, Hakan and Wen, Hongkai (2020) D3P : Data-driven demand prediction for fast expanding electric vehicle sharing systems. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4 (1). 21. doi:10.1145/3381005 ISSN 2474-9567.

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Abstract

The future of urban mobility is expected to be shared and electric. It is not only a more sustainable paradigm that can reduce emissions, but can also bring societal benefits by offering a more affordable on-demand mobility option to the general public. Many car sharing service providers as well as automobile manufacturers are entering the competition by expanding both their EV fleets and renting/returning station networks, aiming to seize a share of the market and to bring car sharing to the zero emissions level. During their fast expansion, one determinant for success is the ability of predicting the demand of stations as the entire system is growing continuously. There are several challenges in this demand prediction problem: First, unlike most of the existing work which predicts demand only for static systems or at few stages of expansion, in the real world we often need to predict the demand as or even before stations are being deployed or closed, to provide information and decision support. Second, for the new stations to be deployed, there is no historical data available to help the prediction of their demand. Finally, the impact of deploying/closing stations on the other stations in the system can be complex. To address these challenges, we formulate the demand prediction problem in the context of fast expanding electric vehicle sharing systems, and propose a data-driven demand prediction approach which aims to model the expansion dynamics directly from the data. We use a local temporal encoding process to handle the historical data for each existing station, and a dynamic spatial encoding process to take correlations between stations into account with Graph Convolutional Neural Networks (GCN). The encoded features are fed to a multi-scale predictor, which forecasts both the long-term expected demand of the stations and their instant demand in the near future. We evaluate the proposed approach with real-world data collected from a major EV sharing platform for one year. Experimental results demonstrate that our approach significantly outperforms the state of the art, showing up to three-fold performance gain in predicting demand for the expanding EV sharing systems.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Journal or Publication Title:	Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Publisher:	ACM
ISSN:	2474-9567
Official Date:	March 2020
Dates:	Date Event March 2020 Published 8 January 2020 Accepted
Volume:	4
Number:	1
Article Number:	21
DOI:	10.1145/3381005
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	© ACM 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4 (1). 21. http://doi.acm.org/10.1145/10.1145/3381005
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	17 April 2020
Date of first compliant Open Access:	17 April 2020
Is Part Of:	1

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