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Identification and classification of public transport activity centres in Stockholm using passenger flows data

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  • Cats, Oded
  • Wang, Qian
  • Zhao, Yu

Abstract

Urban geography could be characterized by analysing the patterns that describe the flows of people and goods. Measuring urban structures is essential for supporting an evidence-based spatial planning policy. The objective of this study is to examine how the spatial–temporal distribution of public transport passenger flow could be used to reveal urban structure dynamics. A methodology to identify and classify centres based on mobility data was applied to Metropolitan Stockholm in Sweden using multi-modal public transport passenger flows. Stockholm is known for its long-term monocentric planning with a dominant central core and radial public transport system. Strategic nodes along its radial public transport system have been a focus for development of sub-centres. Although the regional planning policy embraces a shift towards a polycentric planning policy, the results indicate that this has not been realized insofar.

Suggested Citation

  • Cats, Oded & Wang, Qian & Zhao, Yu, 2015. "Identification and classification of public transport activity centres in Stockholm using passenger flows data," Journal of Transport Geography, Elsevier, vol. 48(C), pages 10-22.
    • Handle: RePEc:eee:jotrge:v:48:y:2015:i:c:p:10-22
    DOI: 10.1016/j.jtrangeo.2015.08.005
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    Cited by:

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    2. Cats, O., 2016. "The robustness value of public transport development plans," Journal of Transport Geography, Elsevier, vol. 51(C), pages 236-246.
    3. Rubensson, Isak & Susilo, Yusak & Cats, Oded, 2020. "Is flat fare fair? Equity impact of fare scheme change," Transport Policy, Elsevier, vol. 91(C), pages 48-58.
    4. Zhou, Yang & Thill, Jean-Claude & Xu, Yang & Fang, Zhixiang, 2021. "Variability in individual home-work activity patterns," Journal of Transport Geography, Elsevier, vol. 90(C).
    5. Cats, Oded & Birch, Nigel, 2021. "Multi-modal network evolution in polycentric regions," Journal of Transport Geography, Elsevier, vol. 96(C).
    6. Cats, Oded & Krishnakumari, Panchamy, 2020. "Metropolitan rail network robustness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    7. Yap, Menno & Munizaga, Marcela, 2018. "Workshop 8 report: Big data in the digital age and how it can benefit public transport users," Research in Transportation Economics, Elsevier, vol. 69(C), pages 615-620.
    8. Pengfei Lin & Jiancheng Weng & Dimitrios Alivanistos & Siyong Ma & Baocai Yin, 2020. "Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    9. Mi-Kyeong Kim & Sangpil Kim & Hong-Gyoo Sohn, 2018. "Relationship between Spatio-Temporal Travel Patterns Derived from Smart-Card Data and Local Environmental Characteristics of Seoul, Korea," Sustainability, MDPI, vol. 10(3), pages 1-18, March.
    10. Zhang, Yuerong & Marshall, Stephen & Manley, Ed, 2021. "Understanding the roles of rail stations: Insights from network approaches in the London metropolitan area," Journal of Transport Geography, Elsevier, vol. 94(C).
    11. Rubensson, Isak & Susilo, Yusak & Cats, Oded, 2020. "Fair accessibility – Operationalizing the distributional effects of policy interventions," Journal of Transport Geography, Elsevier, vol. 89(C).

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