Driving operation state transition prediction under moving bottleneck scenario via Graph-Attention LSTM with Bayesian survival features
- DOI
- 10.1016/j.aap.2026.108844
- Published
- 2027-02
- Container
- Accident Analysis & Prevention
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.aap.2026.108844,
title = {Driving operation state transition prediction under moving bottleneck scenario via Graph-Attention LSTM with Bayesian survival features},
author = {Qiangqiang Shangguan and Menglin Yang and Hongtao Wu and Junhua Wang and Ting Fu},
year = {2027},
journal = {Accident Analysis \& Prevention},
doi = {10.1016/j.aap.2026.108844},
url = {https://doi.org/10.1016/j.aap.2026.108844}
}RIS
TY - JOUR TI - Driving operation state transition prediction under moving bottleneck scenario via Graph-Attention LSTM with Bayesian survival features AU - Qiangqiang Shangguan AU - Menglin Yang AU - Hongtao Wu AU - Junhua Wang AU - Ting Fu PY - 2027 JO - Accident Analysis & Prevention DO - 10.1016/j.aap.2026.108844 UR - https://doi.org/10.1016/j.aap.2026.108844 ER -
APA
Shangguan, Q., Yang, M., Wu, H., Wang, J., & Fu, T. (2027). Driving operation state transition prediction under moving bottleneck scenario via Graph-Attention LSTM with Bayesian survival features. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2026.108844
Source records
- crossref · retrieved 2026-11-10T11:47:44.605Z