Connectivity Contribution to Urban Hub Network Based on Super Network Theory – Case Study of Beijing

Gang, Yuan; Kong, Dewen; Sun, Lishan; Luo, Wei; Xu, Yan

doi:10.7307/ptt.v33i1.3536

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Meanwhile, infrastructure networks have been extensively studied and many valuable insights have also been obtained in recent years. Graphs derived from such networks were analysed on the examples of road networks [5][6], inter-urban transit networks [7][8], airline networks [9][10] and urban transit networks [11][12]. Nevertheless, subway systems present smaller-sized networks compared to most studied transportation networks, they exhibit a rich variety of fascinating network properties, which include scale-free structures [13][14], small-world properties [15][16], network efficiency [17][18], network vulnerability [19][20] and network growth patterns [21][22].…”

Section: Introductionmentioning

confidence: 99%

Weighted Complex Network Analysis of the Difference Between Nodal Centralities of the Beijing Subway System

TONG

Wei

et al. 2022

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The centrality of stations is one of the most important issues in urban transit systems. The central stations of such networks have often been identified using network to-pological centrality measures. In real networks, passenger flows arise from an interplay between the dynamics of the individual person movements and the underlying physical structure. In this paper, we apply a two-layered model to identify the most central stations in the Beijing Subway System, in which the lower layer is the physical infrastruc-ture and the upper layer represents the passenger flows. We compare various centrality indicators such as degree, strength and betweenness centrality for the two-layered model. To represent the influence of exogenous factors of stations on the subway system, we reference the al-pha centrality. The results show that the central stations in the geographic system in terms of the betweenness are not consistent with the central stations in the network of the flows in terms of the alpha centrality. We clarify this difference by comparing the two centrality measures with the real load, indicating that the alpha centrality approx-imates the real load better than the betweenness, as it can capture the direction and volume of the flows along links and the flows into and out of the systems. The empirical findings can give us some useful insights into the node cen-trality of subway systems.

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