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Research Article | Open Access

Reliability of electric vehicle charging infrastructure: A cross-lingual deep learning approach

Yifan Liua,1, Azell Francisb,1, Catharina Hollauerc,1, M. Cade Lawsond, Omar Shaikhe,f, Ashley Cotsmana, Khushi Bhardwaje, Aline Banboukiana, Mimi Lig, Anne Webba, Omar Isaac Asensioa,h( )
School of Public Policy, Georgia Institute of Technology, Atlanta, 30332, USA
Sam Nunn School of International Affairs, Georgia Institute of Technology, Atlanta, 30332, USA
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, 30332, USA
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, 30332, USA
School of Computer Science, Georgia Institute of Technology, Atlanta, 30332, USA
School of Computer Science, Stanford University, Palo Alto, 94305, USA
School of Economics, Georgia Institute of Technology, Atlanta, 30332, USA
Institute for Data Engineering & Science (IDEaS), Georgia Institute of Technology, Atlanta, 30332, USA

1 These authors contributed equally to this work.

Show Author Information

Highlights

• Global EV charging data has poor interoperability & is inaccessible to policymakers.

• A generalizable, cross-lingual machine learning-based model is developed.

• 10-yrs of unstructured ESE Asia data in 72 languages reveals service provision gaps.

• Charging is less reliable at government points of interest than at private locations.

• Consumer concerns include station reliability, availability & location amenities.

Abstract

Vehicle electrification has emerged as a global strategy to address climate change and emissions externalities from the transportation sector. Deployment of charging infrastructure is needed to accelerate technology adoption; however, managers and policymakers have had limited evidence on the use of public charging stations due to poor data sharing and decentralized ownership across regions. In this article, we use machine learning based classifiers to reveal insights about consumer charging behavior in 72 detected languages including Chinese. We investigate 10 years of consumer reviews in East and Southeast Asia from 2011 to 2021 to enable infrastructure evaluation at a larger geographic scale than previously available. We find evidence that charging stations at government locations result in higher failure rates with consumers compared to charging stations at private points of interest. This evidence contrasts with predictions in the U.S. and European markets, where the performance is closer to parity. We also find that networked stations with communication protocols provide a relatively higher quality of charging services, which favors policy support for connectivity, particularly for underserved or remote areas.

Keywords

Natural language processingMachine learningElectric vehiclesConsumer behaviorCharging infrastructurePublic policyTransformer algorithms

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Communications in Transportation Research
Volume 3,
December 2023
Article number: 100095
DOI: 10.1016/j.commtr.2023.100095
Cite this article:
Liu Y, Francis A, Hollauer C, et al. Reliability of electric vehicle charging infrastructure: A cross-lingual deep learning approach. Communications in Transportation Research, 2023, 3: 100095. https://doi.org/10.1016/j.commtr.2023.100095

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Received: 30 September 2022
Revised: 20 February 2023
Accepted: 20 February 2023
Published: 18 April 2023
© 2023 The Authors.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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