Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
The resilience of multimodal transportation networks in urban agglomerations to attacks was analyzed using a network structural resilience assessment model based on complex network theory and resilient city theory which considered the absorbing capacity, buffering ability and recovery ability of the networks. The network resilience was calculated by a space vector modulus. Data for road and railway passenger transport between cities in the Guanzhong Plain urban agglomeration (GZP agglomeration) was used to construct the regional transportation network. The topological characteristics of the multimodal transportation network were then analyzed using the ArcGis and Ucinet tools. Each node's geographical location and transport connections with the surrounding area were used to identify key nodes based on the node importance. Analyses of the effects of attacks on these key nodes in the multimodal transportation network showed that the network had poor buffering ability, especially after failure of the Caijiapo station when the buffer value decreased to 0.388 9. The system had very weak ability to return to the normal state after a site with a large node degree failed. Failures of the general railway station and the high-speed rail station had far greater impacts on the structural resilience than failure of the highway terminals. Analysis of the characteristics of the key nodes led to suggestions for improving the network structure resilience.
SHAO Y W, XU J. Understanding urban resilience: A conceptual analysis based on integrated international literature review[J]. Urban Planning International, 2015, 30(2): 48-54. (in Chinese)
WEI S M, PAN J H. Network structure resilience of cities at the prefecture level and above in China[J]. Acta Geographica Sinica, 2021, 76(6): 1394-1407. (in Chinese)
LIU J, LU H P, MA H, et al. Network vulnerability analysis of rail transit plans in Beijing-Tianjin-Hebei region considering connectivity reliability[J]. Sustainability, 2017, 9(8): 1479.
PENG C, CHEN S Y, WANG B Q. Analyzing city network's structural resilience under disruption scenarios: A case study of passenger transport network in the middle reaches of Yangtze River[J]. Economic Geography, 2019, 39(8): 68-76. (in Chinese)
ZHANG J F, REN G, MA J F, et al. Decision-making method of repair sequence for metro network based on resilience evaluation[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4): 14-20. (in Chinese)
HUANG Y, LIU M R, WEI J G, et al. Research on urban metro network recovery strategy based on resilience curve[J]. Journal of Catastrophology, 2021, 36(1): 32-36. (in Chinese)
ZHANG X. Day-to-day road network vulnerability identification based on network efficiency[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(2): 176-182. (in Chinese)
LI C B, ZHANG S, YANG Z C, et al. Invulnerability simulation in urban agglomeration passenger traffic network under targeted attacks[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(2): 14-21. (in Chinese)
STUBBS-RICHARDSON M S, COSBY A K, BERGENE K D, et al. Searching for safety: Crime prevention in the era of Google[J]. Crime Science, 2018, 7(1): 21.
JI X F, XIE J, WU J Q. Assessment method of expressway resilience considering different intrusion scenes[J]. Journal of Safety Science and Technology, 2019, 15(1): 12-19. (in Chinese)
MA S H, HU M F, GE Y, et al. Application of nodal superiority degree model in optimization of arterial highway network layout[J]. Journal of Highway and Transportation Research and Development, 2016, 33(8): 133-139. (in Chinese)
YAO M R, CHEN Y M, ZHOU Z J, et al. The evolution of structural features and gravity center for China-ASEAN tourist flow network[J]. Economic Geography, 2018, 38(7): 181-189. (in Chinese)