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

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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Bibliographic Details
Main Authors: Qianqi Fan, Chengcheng Yu, Jianyong Zuo
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
public transportation systems
emergency passenger flow prediction
casual inference
deep learning
big data
Online Access:https://www.mdpi.com/2076-3417/15/12/6398
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