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Identifying the spatial distribution of public transportation trips by node and community characteristics

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  • Jun Li
  • Peiqing Zheng
  • Wenna Zhang

Abstract

Identifying the spatial distribution of travel activities can help public transportation managers optimize the allocation of resources. In this paper, transit networks are constructed based on traffic flow data rather than network topologies. The PageRank algorithm and community detection method are combined to identify the spatial distribution of public transportation trips. The structural centrality and PageRank values are compared to identify hub stations; the community detection method is applied to reveal the community structures. A case study in Guangzhou, China is presented. It is found that the bus network has a community structure, significant weekday commuting and small-world characteristics. The metro network is tightly connected, highly loaded, and has no obvious community structure. Hub stations show distinct differences in terms of volume and weekend/weekday usage. The results imply that the proposed method can be used to identify the spatial distribution of urban public transportation and provide a new study perspective.

Suggested Citation

  • Jun Li & Peiqing Zheng & Wenna Zhang, 2020. "Identifying the spatial distribution of public transportation trips by node and community characteristics," Transportation Planning and Technology, Taylor & Francis Journals, vol. 43(3), pages 325-340, April.
    • Handle: RePEc:taf:transp:v:43:y:2020:i:3:p:325-340
    DOI: 10.1080/03081060.2020.1735776
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    Cited by:

    1. Yangyang Meng & Xiaofei Zhao & Jianzhong Liu & Qingjie Qi, 2023. "Dynamic Influence Analysis of the Important Station Evolution on the Resilience of Complex Metro Network," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    2. Yangyang Meng & Qingjie Qi & Jianzhong Liu & Wei Zhou, 2022. "Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    3. 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).
    4. Güner, Samet & İbrahim Cebeci, Halil, 2021. "Multi-period efficiency analysis of major European and Asian airports under fixed proportion technologies," Transport Policy, Elsevier, vol. 107(C), pages 24-42.
    5. Shuaiming Chen & Ximing Ji & Haipeng Shao, 2024. "Revealing the Community Structure of Urban Bus Networks: a Multi-view Graph Learning Approach," Networks and Spatial Economics, Springer, vol. 24(3), pages 589-619, September.
    6. Paul Nailly & Etienne Côme & Latifa Oukhellou & Allou Samé & Jacques Ferriere & Yasmine Merad-Boudia, 2024. "Multivariate count time series segmentation with “sums and shares” and Poisson lognormal mixture models: a comparative study using pedestrian flows within a multimodal transport hub," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(2), pages 455-491, June.

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