By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. 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 electric vehicle (EV) charging stations, we provide national evidence on how well the existing charging infrastructure is serving the needs of the rapidly expanding population of EV drivers in 651 core-based statistical areas in the United States. We deploy supervised machine-learning algorithms to automatically classify unstructured text reviews generated by EV users. Extracting behavioural insights at a population scale has been challenging given that streaming data can be costly to hand classify. Using computational approaches, we reduce processing times for research evaluation from weeks of human processing to just minutes of computation. Contrary to theoretical predictions, we find that stations at private charging locations do not outperform public charging locations provided by the government. Overall, nearly half of drivers who use mobility applications have faced negative experiences at EV charging stations in the early growth years of public charging infrastructure, a problem that needs to be fixed as the market for electrified and sustainable transportation expands.
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We provide the weights of the trained deep-learning models. These datasets generated and/or analysed during the current study are available in the Figshare repository https://doi.org/10.6084/m9.figshare.1204467056. The raw data that support the findings of this study are available from the corresponding author upon request. These data may not be posted publicly due to privacy restrictions. For interested readers, an alternative open data API service with global EV charging infrastructure data is available from OpenChargeMap (https://openchargemap.org/), which is derived from a variety of public sources and contributions. Source Data are provided with this paper.
All custom code and algorithm replication materials have been deposited on the Github repository using Zenodo version releases at https://doi.org/10.5281/zenodo.1419830.
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This research was supported by a grant from the National Science Foundation (CPS award no. 1931980), the Civic Data Science REU programme at Georgia Tech (NSF award no. IIS-1659757), the Anthony and Jeanne Pritzker Family Foundation, the Sustainable LA Grand Challenge. We are grateful to E. Zegura and C. Le Dantec for discussions. For valuable research assistance, we thank M. E. Burke, S. Dharur, S. Oh and D. Marchetto. Special thanks to N. Hajjar.
This research was also supported in part through research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at the Georgia Institute of Technology, Atlanta, Georgia, USA.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The mean test accuracy for 1,000 runs is 84.6% with a S.D. of 0.79. Source data
Counts of machine classified reviews of binary sentiment by public and private ownership. 2,256 reviews were submitted in locations where it was impossible to discern whether it was public or private. Source data
FRM results for the review rate and negativity score.
Counts of machine classified reviews of binary sentiment by geographic area type as defined by U.S. Census designations. Source data
Extended Data Table 4 Probability of negative sentiment for 18 core-based statistical areas in the United States.
Results of t-tests for free and paid stations by public and private ownership in 18 CBSAs in the United States. Source data
Results of t-tests for free and paid stations by public and private ownership in the top 20 states by number of reviews. Source data
Contains state ID and number of reviews.
Contains the weights of the trained CNN model used to create the heatmap figure
Contains location ID, Public or Private designations, Free or Paid Designations
Contains location ID, U.S. Census area type, point of interest, and predicted sentiment
Contains 1000 CNN performance runs with accuracy in percentage
Contains location ID, public or private designations, predicted sentiment
Contains location ID, U.S. Census area type, and predicted sentiment per review
CBSA, free or paid designations, public or private designations, and predicted sentiment per review
Public or private designations, free or paid designations, state, predicted sentiment per review
Contains review ID and human expert labels positive or negative
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Asensio, O.I., Alvarez, K., Dror, A. et al. Real-time data from mobile platforms to evaluate sustainable transportation infrastructure. Nat Sustain 3, 463–471 (2020). https://doi.org/10.1038/s41893-020-0533-6