Technical gazette, Vol. 33 No. 2, 2026.
Original scientific paper
https://doi.org/10.17559/TV-20250227002427
A Graph Network for High-speed Railway Operation Risk Prevention and Control
Yuan Zhao
; 1) School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China 2) Center for Digital Compus, Capital Normal University, Beijing, 100048, China
Qiuyan Zhang
; School of Traffic and Transportation, Beijing Jiaotong University, 100044, Beijing, China
*
Shifeng Liu
; School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China
* Corresponding author.
Abstract
With the rapid expansion of China's high-speed railway (HSR) network, safety concerns in HSR operations have garnered increasing attention. Currently, various railway departments have devised numerous feasible risk control plans. However, these plans predominantly exist in an unstructured textual format, leading to challenges in automation, standardization, content updates, and cross-departmental collaboration. To address these limitations, this study introduces a novel graph-based Risk Scenario Decision (RSD) model, which systematically structures unstructured emergency risk scenarios into an explicit graphical format. The RSD model utilizes graph theory principles and incorporates LLM for precise knowledge extraction and alignment. This approach significantly enhances automation, consistency, and efficiency in railway operational risk management. By transforming traditional risk control plans into a graph-based network, the RSD model facilitates efficient decision-making and rapid response, thereby improving the overall management and effectiveness of HSR operational risk control. Experimental validation demonstrates the high accuracy (up to 98.38%) of the RSD construction process. Ultimately, this research provides a robust, interpretable, and automated framework that substantially enhances proactive risk management in HSR operations, ensuring greater safety and operational efficiency.
Keywords
graph network; high-speed railway; risk prevention and control; risk scenario decision
Hrčak ID:
344975
URI
Publication date:
28.2.2026.
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