Open Access

ARTICLE

A Data-Enhanced Deep Learning Approach for Emergency Domain Question Intention Recognition in Urban Rail Transit

Yinuo Chen¹, Xu Wu¹, Jiaxin Fan¹, Guangyu Zhu^2,*

1 School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China
2 School of Automation and Software Engineering, Shanxi University, Taiyuan, 030006, China

* Corresponding Author: Guangyu Zhu. Email:

(This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)

Computers, Materials & Continua 2025, 84(1), 1597-1613. https://doi.org/10.32604/cmc.2025.062779

Received 27 December 2024; Accepted 17 April 2025; Issue published 09 June 2025

Abstract

The consultation intention of emergency decision-makers in urban rail transit (URT) is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services. This approach facilitates the rapid collection of complete knowledge and rules to form effective decisions. However, the current structured degree of the URT emergency knowledge base remains low, and the domain questions lack labeled datasets, resulting in a large deviation between the consultation outcomes and the intended objectives. To address this issue, this paper proposes a question intention recognition model for the URT emergency domain, leveraging knowledge graph (KG) and data enhancement technology. First, a structured storage of emergency cases and emergency plans is realized based on KG. Subsequently, a comprehensive question template is developed, and the labeled dataset of emergency domain questions in URT is generated through the KG. Lastly, data enhancement is applied by prompt learning and the NLP Chinese Data Augmentation (NLPCDA) tool, and the intention recognition model combining Generalized Auto-regression Pre-training for Language Understanding (XLNet) and Recurrent Convolutional Neural Network for Text Classification (TextRCNN) is constructed. Word embeddings are generated by XLNet, context information is further captured using Bidirectional Long Short-Term Memory Neural Network (BiLSTM), and salient features are extracted with Convolutional Neural Network (CNN). Experimental results demonstrate that the proposed model can enhance the clarity of classification and the identification of domain questions, thereby providing supportive knowledge for emergency decision-making in URT.

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Keywords

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