SUMO-enhanced traffic anomaly detection: enhancing spatiotemporal transferability through simulation-based data augmentation

Traffic anomaly detection is crucial for road safety and traffic efficiency, but the scarcity of accident data poses challenges for model training. Although current research has utilized deep neural networks for data augmentation in small-sample scenarios, it has largely overlooked real-world physic...

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Bibliografiske detaljer

Principais autores:	Haonan Yang, Chenlei Liao, Zhiwu Dong, Chuqiao Chen, Dong Mo, Wenhao Chen
Format:	Artigo
Sprog:	Inglês
Udgivet:	Maximum Academic Press 2025-12-01
Serier:	Digital Transportation and Safety
Fag:	accident prediction and detection spatial and temporal transferability sumo simulation data augmentation freeway safety
Online adgang:	https://www.maxapress.com/article/doi/10.48130/dts-0025-0025
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