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Analysis of community properties and node properties to understand the structure of the bus transport network

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  • Sun, Yeran
  • Mburu, Lucy
  • Wang, Shaohua

Abstract

Akin to most infrastructures, intraurban bus networks are large and highly complex. Understanding the composition of such networks requires an intricate decomposition of the network into modules, taking into account the manner in which network links are distributed among the nodes. There exists for each set of highly interlinked nodes little connectivity with the next set of highly interlinked nodes. This inherent property of nodes makes community detection a popular approach for analyzing the structure of complex networks. In this study, we attempt to understand the structure of the intraurban bus network of Ireland’s capital city, Dublin in a two-step approach. We first analyze the modular structure of the network by identifying potential communities. Secondly, we assess the prominence of each network node by examining the module-based topological properties of the nodes. Results of this empirical study reveal a clear pattern of independent communities, indicating thus, an implicit multi-community structure of the intraurban bus network. Examination of the geographic characteristics of the identified communities shows a degree of socio-economic divisions of the Dublin city. Furthermore, a large majority of the important nodes (vital transportation hubs) are located at the city center, implying that most of the bus lines in Dublin city tend to intersect the city’s core.

Suggested Citation

  • Sun, Yeran & Mburu, Lucy & Wang, Shaohua, 2016. "Analysis of community properties and node properties to understand the structure of the bus transport network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 523-530.
    • Handle: RePEc:eee:phsmap:v:450:y:2016:i:c:p:523-530
    DOI: 10.1016/j.physa.2015.12.150
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    2. Zhang, Hui & Zhuge, Chengxiang & Yu, Xiaohua, 2018. "Identifying hub stations and important lines of bus networks: A case study in Xiamen, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 394-402.
    3. Wei, Sheng & Zheng, Wei & Wang, Lei, 2021. "Understanding the configuration of bus networks in urban China from the perspective of network types and administrative division effect," Transport Policy, Elsevier, vol. 104(C), pages 1-17.
    4. Shiwei Lu & Yaping Huang & Zhiyuan Zhao & Xiping Yang, 2018. "Exploring the Hierarchical Structure of China’s Railway Network from 2008 to 2017," Sustainability, MDPI, vol. 10(9), pages 1-15, September.
    5. Jia, Jianlin & Chen, Yanyan & Wang, Yang & Li, Tongfei & Li, Yongxing, 2021. "A new global method for identifying urban rail transit key station during COVID-19: A case study of Beijing, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    6. Li, Jin-Yang & Teng, Jing & Wang, Hui, 2023. "Integrating bipartite network modelling and overlapping community detection: A new method to evaluate transit line coordination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    7. Wang, Longjian & Zhang, Shuichao & Szűcs, Gábor & Wang, Yonggang, 2024. "Identifying the critical nodes in multi-modal transportation network with a traffic demand-based computational method," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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