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...

詳細記述

書誌詳細

主要な著者:	Qianqi Fan, Chengcheng Yu, Jianyong Zuo
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2025-06-01
シリーズ:	Applied Sciences
主題:	public transportation systems emergency passenger flow prediction casual inference deep learning big data
オンライン･アクセス:	https://www.mdpi.com/2076-3417/15/12/6398
タグ:	タグ追加 タグなし, このレコードへの初めてのタグを付けませんか!