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Title: Evaluating electric vehicle user mobility data using neural network-based language models
By displacing gasoline and diesel fuels, electric cars and fleets offer significant public health benefits by reducing emissions from the transportation sector. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to adoption. Using large-scale social data and machine learning based on 12,720 U.S. electric vehicle charging stations, we provide national evidence on how well the existing charging infrastructure is serving the needs of the expanding population of EV drivers in 651 core-based statistical areas in the United States. Contrary to predictions, we find that stations at private charging locations do not outperform public charging locations provided by government. We also find evidence of higher negative sentiment in the dense urban centers, where issues of charge rage and congestion may be the most prominent. Overall, 40% of drivers using mobility apps have faced negative experiences at EV charging stations, a problem that needs to be fixed as the market expands.  more » « less
Award ID(s):
1659757
NSF-PAR ID:
10086805
Author(s) / Creator(s):
Date Published:
Journal Name:
Proceedings of the Transportation Research Board (TRB) annual meeting
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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