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How transit scaling shapes cities

Author

Listed:
  • Hao Wu

    (University of Sydney)

  • David Levinson

    (University of Sydney)

  • Somwrita Sarkar

    (University of Sydney)

Abstract

Transit accessibility to jobs (the ease of reaching a place of work by public transport) affects both residential location and commute mode choice, resulting in gradations of residential land-use intensity and transit (public transport) patronage. We propose a scaling model explaining much of the variation in transit use—the number of transit commuters per km2—and residential land-use intensity with transit accessibility. We find that locations with high transit accessibility consistently have more riders and higher residential density; transit systems that provide greater accessibility and with a larger base for patronage have proportionally greater ridership increase per unit of accessibility. All 48 metropolitan statistical areas in our sample have a scaling factor less than 1, so a 1% increase in access to jobs produces a less than 1% increase in transit riders; the largest cities therefore have higher scaling factors than smaller cities, indicating returns to scale. The models, derived from a new database of transit accessibility measured for every minute of the peak period over 11 million US census-blocks, and estimated for 48 major cities across the United States, find that the number of jobs reachable within 45 minutes of the rider’s base most affect transit rider density. The findings support the idea that transit investment should focus on mature, well-developed regions.

Suggested Citation

  • Hao Wu & David Levinson & Somwrita Sarkar, 2019. "How transit scaling shapes cities," Nature Sustainability, Nature, vol. 2(12), pages 1142-1148, December.
  • Handle: RePEc:nat:natsus:v:2:y:2019:i:12:d:10.1038_s41893-019-0427-7
    DOI: 10.1038/s41893-019-0427-7
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    Citations

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    Cited by:

    1. Élise Labonté-LeMoyne & Shang-Lin Chen & Constantinos K. Coursaris & Sylvain Sénécal & Pierre-Majorique Léger, 2020. "The Unintended Consequences of COVID-19 Mitigation Measures on Mass Transit and Car Use," Sustainability, MDPI, vol. 12(23), pages 1-13, November.
    2. Zhang, Hui & Zhan, Bo & Ouyang, Min, 2024. "Enhancing accessibility through rail transit in congested urban areas: A cross-regional analysis," Journal of Transport Geography, Elsevier, vol. 115(C).
    3. Cui, Boer & DeWeese, James & Wu, Hao & King, David A. & Levinson, David & El-Geneidy, Ahmed, 2022. "All ridership is local: Accessibility, competition, and stop-level determinants of daily bus boardings in Portland, Oregon," Journal of Transport Geography, Elsevier, vol. 99(C).
    4. Berrebi, Simon J. & Joshi, Sanskruti & Watkins, Kari E., 2021. "On bus ridership and frequency," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 140-154.
    5. Simon Berrebi & Sanskruti Joshi & Kari E Watkins, 2020. "On Ridership and Frequency," Papers 2002.02493, arXiv.org, revised Apr 2021.
    6. Wu, Hao & Lee, Jinwoo (Brian) & Levinson, David, 2023. "The node-place model, accessibility, and station level transit ridership," Journal of Transport Geography, Elsevier, vol. 113(C).
    7. Hao Wu & David Levinson, 2020. "Unifying Access," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    8. David Levinson & Hao Wu, 2020. "Towards a general theory of access," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    9. Slavko, Bohdan & Glavatskiy, Kirill S. & Prokopenko, Mikhail, 2021. "Revealing configurational attractors in the evolution of modern Australian and US cities," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    10. Hao Wu & Paolo Avner & Genevieve Boisjoly & Carlos K. V. Braga & Ahmed El-Geneidy & Jie Huang & Tamara Kerzhner & Brendan Murphy & Michał A. Niedzielski & Rafael H. M. Pereira & John P. Pritchard & A, 2022. "Urban access across the globe: an international comparison of different transport modes," Working Papers 2021-01, University of Minnesota: Nexus Research Group.

    More about this item

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns

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