PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference
- DOI
- 10.1016/j.aap.2023.107174
- Published
- 2023-10
- Container
- Accident Analysis & Prevention
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.aap.2023.107174,
title = {PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference},
author = {Qiuyang Huang and Hongfei Jia and Zhilu Yuan and Ruiyi Wu},
year = {2023},
journal = {Accident Analysis \& Prevention},
doi = {10.1016/j.aap.2023.107174},
url = {https://doi.org/10.1016/j.aap.2023.107174}
}RIS
TY - JOUR TI - PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference AU - Qiuyang Huang AU - Hongfei Jia AU - Zhilu Yuan AU - Ruiyi Wu PY - 2023 JO - Accident Analysis & Prevention DO - 10.1016/j.aap.2023.107174 UR - https://doi.org/10.1016/j.aap.2023.107174 ER -
APA
Huang, Q., Jia, H., Yuan, Z., & Wu, R. (2023). PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2023.107174
Source records
- crossref · retrieved 2026-09-25T23:23:53.512Z