Disentangling the latent representation of traffic conflicts: A Multi-Task Ordinal Bayesian Variational AutoEncoder approach
- DOI
- 10.1016/j.aap.2026.108831
- Published
- 2027-01
- Container
- Accident Analysis & Prevention
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.aap.2026.108831,
title = {Disentangling the latent representation of traffic conflicts: A Multi-Task Ordinal Bayesian Variational AutoEncoder approach},
author = {Yue Liu and Guohua Liang and Baojie Wang and Ziyu Chen and Zhixiang Gao},
year = {2027},
journal = {Accident Analysis \& Prevention},
doi = {10.1016/j.aap.2026.108831},
url = {https://doi.org/10.1016/j.aap.2026.108831}
}RIS
TY - JOUR TI - Disentangling the latent representation of traffic conflicts: A Multi-Task Ordinal Bayesian Variational AutoEncoder approach AU - Yue Liu AU - Guohua Liang AU - Baojie Wang AU - Ziyu Chen AU - Zhixiang Gao PY - 2027 JO - Accident Analysis & Prevention DO - 10.1016/j.aap.2026.108831 UR - https://doi.org/10.1016/j.aap.2026.108831 ER -
APA
Liu, Y., Liang, G., Wang, B., Chen, Z., & Gao, Z. (2027). Disentangling the latent representation of traffic conflicts: A Multi-Task Ordinal Bayesian Variational AutoEncoder approach. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2026.108831
Source records
- crossref · retrieved 2026-11-10T12:15:55.500Z