Research on the Resilience of a Railway Network Based on a Complex Structure Analysis of Physical and Service Networks

This study examines railway network resilience through a complex network analysis of both the physical infrastructure and service operations. The objectives were to quantify the resilience characteristics of China’s railway network, assess its performance under different disturbance types, and evaluate recovery strategy effectiveness. Network resilience was measured through accessibility, invulnerability, and restorability dimensions. The simulation results demonstrate that targeted disruptions cause significantly more damage than random disturbances, particularly when high-betweenness nodes are affected. Statistical analysis confirmed that betweenness-based recovery strategies outperform degree-based and random approaches. These findings provide a quantitative foundation for infrastructural planning, emergency response protocols, and resource allocation in railway systems.

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Publication Details

Journal
Applied Sciences
Published
2025-05-05
DOI
https://doi.org/10.3390/app15095135
Citations
2
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
2.65

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article

Research on the Resilience of a Railway Network Based on a Complex Structure Analysis of Physical and Service Networks

Kaiqi Sun, Junhua Chen, Tiansheng Dong, Yizhang Li
2 citations
Applied Sciences
Complex Network Analysis Techniques
2.65
article

Research on the Resilience of a Railway Network Based on a Complex Structure Analysis of Physical and Service Networks

Kaiqi Sun, Junhua Chen, Tiansheng Dong, Yizhang Li
article en
2 citations

Abstract

This study examines railway network resilience through a complex network analysis of both the physical infrastructure and service operations. The objectives were to quantify the resilience characteristics of China’s railway network, assess its performance under different disturbance types, and evaluate recovery strategy effectiveness. Network resilience was measured through accessibility, invulnerability, and restorability dimensions. The simulation results demonstrate that targeted disruptions cause significantly more damage than random disturbances, particularly when high-betweenness nodes are affected. Statistical analysis confirmed that betweenness-based recovery strategies outperform degree-based and random approaches. These findings provide a quantitative foundation for infrastructural planning, emergency response protocols, and resource allocation in railway systems.

Applied SciencesVol. 15(9)
Beijing Jiaotong University (CN), China Railway Corporation (CN)
China Railway
Openalex Percentile: Top 11%
Complex Network Analysis Techniques
2.65
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