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How transit scaling shapes cities

Nature Sustainability volume 2, pages 1142–1148 (2019)Cite this article

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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.

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Fig. 1: Scaling coefficients for transit commuter and working population density.
Fig. 2: Transit use trend estimation based on two scaling models of transit accessibility (45 min).
Fig. 3: Commuter density and transit accessibility to jobs.

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Data availability

The transit accessibility data are obtained from the Accessibility Observatory at the University of Minnesota. Block-group accessibility measures are population weighted averages of constituent block values to match the spatial reporting of the mode share data from the US Census Bureau. Working population characteristics come from the 2016 American Community Survey and Longitudinal Employer-Household Dynamics programme’s 2015 Origin-Destination Employment Statistics65,66. Data on mode share are obtained from the 2016 American Community Survey 5 yr estimates which describes commute mode choice between 2012 and 2016. Transit is defined to include walking, ferry, rail (subway and commuter), and bus, trolley and streetcar. Data described in this section are available via public websites.

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Acknowledgements

We thank the Accessibility Observatory at the University of Minnesota for the provision of data.

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  1. University of Sydney, Sydney, New South Wales, Australia

    Hao Wu, David Levinson & Somwrita Sarkar

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  1. Hao Wu
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Contributions

D.L. created the study conception and design, H.W. and S.S. analysed and intepreted the results and H.W. prepared the manuscript.

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Correspondence to Hao Wu.

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Wu, H., Levinson, D. & Sarkar, S. How transit scaling shapes cities. Nat Sustain 2, 1142–1148 (2019). https://doi.org/10.1038/s41893-019-0427-7

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