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Unveiling the rich-club phenomenon in urban mobility networks through the spatiotemporal characteristics of passenger flow

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  • Zhang, Yifan
  • Ng, S. Thomas

Abstract

With increasing passenger flows and rapid urbanization, the Mass Transit Railway (MTR) systems in Hong Kong have entered into an era of complex networking operation, which enables people to travel across 18 local districts and enhance sustainable transport accessibility. The urban mobility flow patterns typically encoded as origin–destination (OD) matrices inspire the constitution of the temporal urban mobility networks (TUMNs), which are represented as the directed weighted networks. The dynamics of the TUMNs have significant implications for transportation systems, urban traffic planning, and mobility pattern exploration. This paper provides a thorough analysis of the rich-club phenomenon of TUMNs in Hong Kong during different time periods. Additionally, the space–time characteristics of passenger flow are analyzed and based on which we build the directed weighted TUMNs where the nodes are the stations, and the weighted links represent the number of trips between nodes. By using the outgoing-strength and incoming-strength global rich-club coefficient, local rich-club coefficient, community detection, and assortativity coefficient, we quantitatively identify the rich-club phenomenon in the TUMNs during both the peak hours (8:00–9:00 and 18:00–19:00) and non-peak hours (10:00–11:00, 15:00–16:00, and 20:00–21:00). The findings uncover the rich-club phenomenon in the TUMNs, and the distribution of the rich-club members evolving over time. The findings of this research pave the way towards a richer spectrum of complex real-world networks and are useful for transport infrastructure planning.

Suggested Citation

  • Zhang, Yifan & Ng, S. Thomas, 2021. "Unveiling the rich-club phenomenon in urban mobility networks through the spatiotemporal characteristics of passenger flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    • Handle: RePEc:eee:phsmap:v:584:y:2021:i:c:s0378437121006506
    DOI: 10.1016/j.physa.2021.126377
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    3. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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