THE EVOLVING RESILIENCE OF GLOBAL METRO NETWORKS

АУТОР(И) / AUTHOR(S): Yun-Hao Dong, Xiao-Wei Luo, Fang-Le Peng, Wout Broere

Download Full Pdf   

DOI:  10.46793/ACUUS2025.3.3.16

САЖЕТАК / ABSTRACT:

Metro systems are essential for urban functionality worldwide, and their resilience is a growing concern. While network analysis has offered insights into structural resilience, a comprehensive understanding of dynamic resilience, particularly its temporal evolution in expanding networks and response to multifaceted disruptions, remains underdeveloped. Previous research has often focused on large systems in a few megacities, neglecting smaller networks. This study addresses these gaps by introducing a novel framework to evaluate the dynamic resilience of evolving metro networks. We compiled data for twelve global cities, modeling their metro systems as evolving complex networks, incorporating geographic coordinates, station opening dates, and catchment area population data. A key contribution is a comprehensive resilience metric that integrates network serviceability, based on population-weighted global efficiency, and quantifies vulnerability as the rate of efficiency loss per disrupted node. We formulated three universally applicable disruption scenarios, including critical node, critical region, and critical line disruptions. Each disruption was simulated at both light (10% node removal) and heavy (20% node removal) intensities, reflecting diverse real-world disruption scenarios. These strategies leverage a comprehensive node centrality index derived from degree, closeness, and betweenness centralities, with edge weights based on reciprocal Euclidean geodesic distances. Applying this framework, we analyzed the resilience evolution across the 12 case study cities, uncovering distinct and common patterns. Findings indicate that dynamic resilience provides critical insights complementary to static efficiency measures and that resilience trajectories are highly dependent on disruption size, intensity, and city-specific network characteristics. This study offers a robust methodology for assessing metro network resilience evolution, providing data-driven insights to enhance the robustness of critical public transport systems and inform strategies for developing more resilient cities.

КЉУЧНЕ РЕЧИ / KEYWORDS:

evolving resilience, metro network, serviceability, disruption scenarios

ПРОЈЕКАТ / ACKNOWLEDGEMENT:

This work was supported by the National Natural Science Foundation of China (NSFC) [grant number: 42301289], and Hong Kong Scholars Program [grant number: XJ2023059].

ЛИТЕРАТУРА / REFERENCES:

• An, D. D., Tong, X., Liu, K., & Chan, E. H. W. (2019). Understanding the impact of built environment on metro ridership using open source in Shanghai. Cities, 93, 177-187. https://doi.org/10.1016/j.cities.2019.05.013
• Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051-13069. https://doi.org/10.1016/j.eswa.2012.05.056
• Derrible, S., & Kennedy, C. (2010). The complexity and robustness of metro networks. Physica a-Statistical Mechanics and Its Applications, 389(17), 3678-3691. https://doi.org/10.1016/j.physa.2010.04.008
• Dong, Y. H., Peng, F. L., & Guo, T. F. (2021). Quantitative assessment method on urban vitality of metro-led underground space based on multi-source data: A case study of Shanghai Inner Ring area. Tunnelling and Underground Space Technology, 116, 104108. https://doi.org/10.1016/j.tust.2021.104108
• Dong, Y. H., Peng, F. L., Li, H., & Men, Y. Q. (2023). Spatiotemporal characteristics of Chinese metro-led underground space development: A multiscale analysis driven by big data. Tunnelling and Underground Space Technology, 139, 105209. https://doi.org/10.1016/j.tust.2023.105209
• Dong, Y. H., Peng, F. L., Zha, B. H., Qiao, Y. K., & Li, H. (2022). An intelligent layout planning model for underground space surrounding metro stations based on NSGA-II. Tunnelling and Underground Space Technology, 128,104648. https://doi.org/10.1016/j.tust.2022.104648
• Du, Y. X., Gao, C., Hu, Y., Mahadevan, S., & Deng, Y. (2014). A new method of identifying influential nodes in complex networks based on TOPSIS. Physica a-Statistical Mechanics and Its Applications, 399, 57-69. https://doi.org/10.1016/j.physa.2013.12.031
• Gonzalez-Navarro, M., & Turner, M. A. (2018). Subways and urban growth: Evidence from earth. Journal of Urban Economics, 108, 85-106. https://doi.org/10.1016/j.jue.2018.09.002
• Hagberg, A., Swart, P. J., & Schult, D. A. (2008). Exploring network structure, dynamics, and function using NetworkX.
• Ingvardson, J. B., & Nielsen, O. A. (2018). How urban density, network topology and socio-economy influence public transport ridership: Empirical evidence from 48 European metropolitan areas. Journal of Transport Geography, 72, 50-63. https://doi.org/10.1016/j.jtrangeo.2018.07.002
• Kanwar, K., Kumar, H., & Kaushal, S. (2019). Complex network based comparative analysis of Delhi Metro network and its extension. Physica a-Statistical Mechanics and Its Applications, 526, 120991. https://doi.org/10.1016/j.physa.2019.04.227
• Lin, D., Broere, W., & Cui, J. Q. (2022). Metro systems and urban development: Impacts and implications. Tunnelling and Underground Space Technology, 125, 104509. https://doi.org/10.1016/j.tust.2022.104509
• Lin, D., Nelson, J. D., Beecroft, M., & Cui, J. Q. (2021). An overview of recent developments in China’s metro systems. Tunnelling and Underground Space Technology, 111, 103783. https://doi.org/10.1016/j.tust.2020.103783
• Lloyd, C. T., Sorichetta, A., & Tatem, A. J. (2017). High resolution global gridded data for use in population studies. Scientific Data, 4, 170001. https://doi.org/10.1038/sdata.2017.1
• Lyu, G., Bertolini, L., & Pfeffer, K. (2016). Developing a TOD typology for Beijing metro station areas. Journal of Transport Geography, 55, 40-50. https://doi.org/10.1016/j.jtrangeo.2016.07.002
• Pei, A. H., Xiao, F., Yu, S. B., & Li, L. L. (2022). Efficiency in the evolution of metro networks. Scientific Reports, 12(1), 8326. https://doi.org/10.1038/s41598-022-12053-3
• Singh, Y. J., Lukman, A., Flacke, J., Zuidgeest, M., & Van Maarseveen, M. F. A. M. (2017). Measuring TOD around transit nodes – Towards TOD policy. Transport Policy, 56, 96-111. https://doi.org/10.1016/j.tranpol.2017.03.013
• Sun, X. H., Liu, Y., Mi, Y. M., & Lv, K. (2024). Identification of key stations and routes in urban metro and conventional bus networks from a resilience perspective. Complex Systems and Complexity Science, 1-9 (in Chinese).
• Yu, X. Y., Chen, Z., Liu, F., & Zhu, H. H. (2023). How urban metro networks grow: From a complex network perspective. Tunnelling and Underground Space Technology, 131, 104841. https://doi.org/10.1016/j.tust.2022.104841
• Zhang, H. R. (2020). Metro and urban growth: Evidence from China. Journal of Transport Geography, 85, 102732. https://doi.org/10.1016/j.jtrangeo.2020.102732