Identifying policy-relevant traffic crash risk factors in Cheongju, South Korea using logistic regression and explainable machine learning.

Lee EJ, Kim S, Lee HJ, Jhong JH

DOI: 10.1371/journal.pone.0350616
Published: 2026
Container: PloS one
Publisher: Not recorded
Open access: yes

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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0350616,
  title = {Identifying policy-relevant traffic crash risk factors in Cheongju, South Korea using logistic regression and explainable machine learning.},
  author = {Lee EJ and Kim S and Lee HJ and Jhong JH},
  year = {2026},
  journal = {PloS one},
  doi = {10.1371/journal.pone.0350616},
  url = {https://doi.org/10.1371/journal.pone.0350616}
}

RIS

TY  - JOUR
TI  - Identifying policy-relevant traffic crash risk factors in Cheongju, South Korea using logistic regression and explainable machine learning.
AU  - Lee EJ
AU  - Kim S
AU  - Lee HJ
AU  - Jhong JH
PY  - 2026
JO  - PloS one
DO  - 10.1371/journal.pone.0350616
UR  - https://doi.org/10.1371/journal.pone.0350616
ER  -

APA

EJ, L., S, K., HJ, L., & JH, J. (2026). Identifying policy-relevant traffic crash risk factors in Cheongju, South Korea using logistic regression and explainable machine learning.. PloS one. https://doi.org/10.1371/journal.pone.0350616

Source records

pubmed · retrieved 2026-09-27T09:40:47.729Z