Predicting Urban Rail Transit Network Origin–Destination Matrix Under Operational Incidents with Deep Counterfactual Inference

The rapid expansion of urban rail networks has resulted in increasingly complex passenger flow patterns, presenting significant challenges for operational management, especially during incidents and emergencies. Disruptions such as power equipment failures, trackside faults, and train malfunctions c...

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Principais autores:	Qianqi Fan, Chengcheng Yu, Jianyong Zuo
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2025-06-01
coleção:	Applied Sciences
Assuntos:	public transportation systems emergency passenger flow prediction casual inference deep learning big data
Acesso em linha:	https://www.mdpi.com/2076-3417/15/12/6398
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