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Real-time data from mobile platforms to evaluate sustainable transportation infrastructure

Nature Sustainability volume 3, pages 463–471 (2020)Cite this article

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Abstract

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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Fig. 1: US map of active charge station reviews.
Fig. 2: Model architecture for the CNN.
Fig. 3: Saliency heatmap for reviews with a domain-specific term ‘iced’.
Fig. 4: Predicted probability of negative sentiment in public and private spaces.
Fig. 5: Predicted probability of negative sentiment by geographical area and POI.

Data availability

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.

Code availability

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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Acknowledgements

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.

Author information

Authors and Affiliations

  1. School of Public Policy and Institute for Data Engineering and Science (IDEaS), Georgia Institute of Technology, Atlanta, GA, USA

    Omar Isaac Asensio

  2. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

    Kevin Alvarez

  3. Department of Statistical and Data Sciences and Department of Government, Smith College, Northampton, MA, USA

    Arielle Dror

  4. Department of Computer Science, Tufts University, Medford, MA, USA

    Emerson Wenzel

  5. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Catharina Hollauer

  6. School of Civil and Environmental Engineering and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Sooji Ha

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Contributions

O.I.A. directed the research and wrote the paper; A.D., E.W. and K.A. developed code, analysed data and wrote the paper; K.A., C.H. and S.H. implemented algorithms and performed experiments; S.H. investigated model validation and interpretability. All authors reviewed the manuscript.

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Correspondence to Omar Isaac Asensio.

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Extended data

Extended Data Fig. 1 Distribution of CNN classifier predictions for 1,000 model runs.

The mean test accuracy for 1,000 runs is 84.6% with a S.D. of 0.79.

Source data

Extended Data Table 1 Descriptive statistics, public and private.

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

Extended Data Table 2 Main results.

FRM results for the review rate and negativity score.

Extended Data Table 3 Descriptive statistics, urban and rural.

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

Extended Data Table 5 Probability of negative sentiment for top 20 U.S. states.

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

Extended Data Table 6 Balance of training data.

Counts of positive and negative reviews by two human annotators (κ=0.84).

Source data

Supplementary information

Supplementary Information

Supplementary discussion, Fig. 1, Tables 1–7 and references.

Source data

Source Data Fig. 1

Contains state ID and number of reviews.

Source Data Fig. 3

Contains the weights of the trained CNN model used to create the heatmap figure

Source Data Fig. 4

Contains location ID, Public or Private designations, Free or Paid Designations

Source Data Fig. 5

Contains location ID, U.S. Census area type, point of interest, and predicted sentiment

Source Data Extended Data Fig. 1

Contains 1000 CNN performance runs with accuracy in percentage

Source Data Extended Data Table 1

Contains location ID, public or private designations, predicted sentiment

Source Data Extended Data Table 3

Contains location ID, U.S. Census area type, and predicted sentiment per review

Source Data Extended Data Table 4

CBSA, free or paid designations, public or private designations, and predicted sentiment per review

Source Data Extended Data Table 5

Public or private designations, free or paid designations, state, predicted sentiment per review

Source Data Extended Data Table 6

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

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