An enhanced framework for automated vehicle crash severity prediction under unobserved heterogeneity using natural language processing and data augmentation.
- DOI
- 10.1080/15389588.2026.2740147
- Published
- 2026 Oct 14
- Container
- Traffic injury prevention
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1080/15389588.2026.2740147,
title = {An enhanced framework for automated vehicle crash severity prediction under unobserved heterogeneity using natural language processing and data augmentation.},
author = {Lee S and Park N and Kim H and Park J},
year = {2026},
journal = {Traffic injury prevention},
doi = {10.1080/15389588.2026.2740147},
url = {https://doi.org/10.1080/15389588.2026.2740147}
}RIS
TY - JOUR TI - An enhanced framework for automated vehicle crash severity prediction under unobserved heterogeneity using natural language processing and data augmentation. AU - Lee S AU - Park N AU - Kim H AU - Park J PY - 2026 JO - Traffic injury prevention DO - 10.1080/15389588.2026.2740147 UR - https://doi.org/10.1080/15389588.2026.2740147 ER -
APA
S, L., N, P., H, K., & J, P. (2026). An enhanced framework for automated vehicle crash severity prediction under unobserved heterogeneity using natural language processing and data augmentation.. Traffic injury prevention. https://doi.org/10.1080/15389588.2026.2740147
Source records
- pubmed · retrieved 2026-11-10T13:46:39.713Z