Complex system evolution of structural resilience, propagation dynamics and intelligent governance in China’s railway network based on multi-layer coupled networks
Research Article
Open Access
CC BY

Complex system evolution of structural resilience, propagation dynamics and intelligent governance in China’s railway network based on multi-layer coupled networks

Yonghao Yang 1*
1 School of Computer Science, Faculty of Engineering, The University of Bristol
*Corresponding author: rr23430@bristol.ac.uk
Published on 29 September 2025
Journal Cover
AEI Vol.16 Issue 9
ISSN (Print): 2977-3911
ISSN (Online): 2977-3903
Download Cover

Abstract

Against the backdrop of China's high-speed rail (HSR) network achieving global leadership in scale (48,000km, 70% worldwide) yet facing structural fragility, disaster-chain coupling, and regional imbalance, this study constructs a "structural resilience-propagation dynamics-intelligent governance" trinity framework. By integrating multi-layer coupled network theory, this paper develop: (1).A spatiotemporal-weighted betweenness centrality algorithm dynamically identifying critical nodes using Beidou data; (2).A multi-layer SEIR model quantifying epidemic-economic dual diffusion with linear threshold mechanisms; (3).A reinforcement learning framework optimizing anti-siphoning policies. Key findings reveal: Wuhan hub fault recovery time reduced by 50% through backup topology, Beijing West Station achieved 89% epidemic blocking via dual-channel quarantine, and the Chengdu-Chongqing regional Gini coefficient decreased 29% via frequency regulation. The paradigm shift from static planning to real-time adaptive governance offers a "China solution" for global railway resilience, evidenced by magnetic levitation standards (e.g., 18% energy savings in Saudi NEOM line) and multi-disaster defense systems (35% faster ASEAN flood recovery).

Keywords:

multi-layer coupled networks, structural resilience, propagation dynamics, intelligent governance, high-speed rail network, spatiotemporal adaptation

View PDF
Yang,Y. (2025). Complex system evolution of structural resilience, propagation dynamics and intelligent governance in China’s railway network based on multi-layer coupled networks. Advances in Engineering Innovation,16(9),59-68.

References

[1]. The Central Committee of the Communist Party of China and the State Council issued the "National New-Type Urbanization Plan (2014-2020)" - No. 9 of the State Council Gazette in 2014. (March 14, 2014). www.gov.cn. https: //www.gov.cn/gongbao/content/2014/content_2644805.htm(in Chinese)

[2]. Barabási, A.-L., & Pósfai, M. (2016). Network science. Cambridge University Press.

[3]. Yagan, O., Eletreby, R., & Gligor, V. (2023). Multi-strain spreading in clustered networks.2023 IEEE International Conference on Communications (ICC), 4210–4215.

[4]. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.

[5]. Forrester, J. W. (1961). Industrial dynamics. MIT Press.

[6]. Zhang, M., Wang, X., Jin, L., & Song, M. (2021). Cascade phenomenon in multilayer networks with dependence groups and hierarchical structure.Physica a Statistical Mechanics and Its Applications, 581, 126201–126201. https: //doi.org/10.1016/j.physa.2021.126201

[7]. Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics.Proceedings of the Royal Society A, 115(772), 700–721.

[8]. Granovetter, M. (1978). Threshold models of collective behavior.American Journal of Sociology, 83(6), 1420–1443.

[9]. Liu, Y., Wang, Q., & Zhang, H. (2023). Dynamic threshold model for regional economic diffusion.Transportation Research Part E: Logistics and Transportation Review, 172, 103065.

[10]. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II.IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

[11]. ReferencesResilient transportation systems for extreme weather events and climate change. (2026, April 28).Nature. https: //www.nature.com/collections/faddigefgc

References

[1]. The Central Committee of the Communist Party of China and the State Council issued the "National New-Type Urbanization Plan (2014-2020)" - No. 9 of the State Council Gazette in 2014. (March 14, 2014). www.gov.cn. https: //www.gov.cn/gongbao/content/2014/content_2644805.htm(in Chinese)

[2]. Barabási, A.-L., & Pósfai, M. (2016). Network science. Cambridge University Press.

[3]. Yagan, O., Eletreby, R., & Gligor, V. (2023). Multi-strain spreading in clustered networks.2023 IEEE International Conference on Communications (ICC), 4210–4215.

[4]. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.

[5]. Forrester, J. W. (1961). Industrial dynamics. MIT Press.

[6]. Zhang, M., Wang, X., Jin, L., & Song, M. (2021). Cascade phenomenon in multilayer networks with dependence groups and hierarchical structure.Physica a Statistical Mechanics and Its Applications, 581, 126201–126201. https: //doi.org/10.1016/j.physa.2021.126201

[7]. Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics.Proceedings of the Royal Society A, 115(772), 700–721.

[8]. Granovetter, M. (1978). Threshold models of collective behavior.American Journal of Sociology, 83(6), 1420–1443.

[9]. Liu, Y., Wang, Q., & Zhang, H. (2023). Dynamic threshold model for regional economic diffusion.Transportation Research Part E: Logistics and Transportation Review, 172, 103065.

[10]. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II.IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

[11]. ReferencesResilient transportation systems for extreme weather events and climate change. (2026, April 28).Nature. https: //www.nature.com/collections/faddigefgc

Cite this article

Yang,Y. (2025). Complex system evolution of structural resilience, propagation dynamics and intelligent governance in China’s railway network based on multi-layer coupled networks. Advances in Engineering Innovation,16(9),59-68.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Journal: Advances in Engineering Innovation

Volume number: Vol.16
Issue number: Issue 9
ISSN: 2977-3903(Print) / 2977-3911(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

For authors
For editors
For reviewers

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.