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Spatial distribution of foot traffic in New York City and applications for urban planning

Nature Cities volume 3, pages 136–145 (2026)Cite this article

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Abstract

Walking is the most prevalent yet least systematically measured mode of urban travel. We present a first city-wide foot-traffic model for peak travel periods in New York City and examine the model’s use as a baseline for targeted infrastructure investments and hazard analysis in urban planning. Comparing estimated pedestrian volumes with the city’s official street classifications shows that many streets in the outer boroughs experience foot-traffic levels comparable with those in central Manhattan but remain undercategorized for pedestrian priority, highlighting potential inequities in infrastructure investment. Linking pedestrian volumes with crash data further shows that intersections with the highest pedestrian injury risk are often outside Manhattan, where exposure-adjusted danger is the greatest. Our findings demonstrate how a systematic analysis of pedestrian volumes can uncover hidden patterns of accessibility, inequity and risk, providing a foundation for more inclusive and evidence-based urban design and policy.

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Fig. 1: Permutation importances during different time periods.
Fig. 2: Spatial distribution of estimated foot-traffic volumes in NYC during the weekday AM peak period.
Fig. 3: Box plots of estimated hourly pedestrian volumes (horizontal axis) for the weekday PM peak period across NYC’s corridor types.
Fig. 4: Corridor types and underclassified street segments.
Fig. 5: Pedestrian crash counts versus exposure-adjusted risk.

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Data availability

The street network data, along with estimated pedestrian flows by trip type, and calibrated volumes for the six studied time periods and calibrated model files in the PCKL format are available to download as Supplementary Data files. Please see Methods for the sources of data used in this study.

Change history

  • 04 March 2026

    Since the version of the article initially published, Supplementary Data 1 Metadata has been uploaded as a Supplementary file to online version of the article.

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Acknowledgements

We would like to thank the NYC DOT and NYC DCP for their encouragement and participation in this research, particularly M. Seaman, Senior Economist at the Office of the Transportation Commissioner. We would also like to thank K. Becerril, who worked as a research assistant on compiling pedestrian crash data for NYC and correcting the pedestrian network, as well as all the current and former MIT City Form Lab members who helped digitize the pedestrian count data sheets from NYC.

Author information

Authors and Affiliations

  1. Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA

    Andres Sevtsuk, Liu Liu, Abdulaziz Alhassan & Justin Kollar

  2. School of City and Regional Planning, Georgia Institute of Technology, Atlanta, GA, USA

    Rounaq Basu

Authors
  1. Andres Sevtsuk
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  2. Rounaq Basu
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  3. Liu Liu
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  4. Abdulaziz Alhassan
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  5. Justin Kollar
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Contributions

A.S. conceived of the research and A.S. and R.B. designed the research implementation. A.A. implemented the model in Python, and estimated the pedestrian flows with advice from all other authors. J.K. used the pedestrian volume estimates to analyze traffic crashes and produce the related figures. L.L. produced the pedestrian flow figures and visual comparisons of the estimated volumes with observed counts. R.B. produced the model calibration with advice from A.S. A.S. led the writing of the paper with editing and contributions from R.B. and J.K. All other co-authors performed revisions.

Corresponding author

Correspondence to Andres Sevtsuk.

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The authors declare no competing interests.

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Nature Cities thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1

Average pedestrian volumes during theweekday evening peak period (5-6PM) in 59Community Districts throughout NYC. Map data from OpenStreetMap under an ODbL license.

Extended Data Fig. 2

Weekday evening peak (5-6PM) foot-traffic estimates around (a) the Bronx Hub, (b) Manhattan’s Canal Street, (c) 7th Ave and 58th St in Brooklyn, and (d) the Flood Triangle in the Bronx. White labels denote model predictions, while blue labels mark observed pedestrian counts during the same time periods in 2018 and 2019 on select segments (data from NYC DOT). Map data from OpenStreetMap under an ODbL license.

Extended Data Fig. 3

31st Street at 23rd Avenue in Queens. Image from daniel_solow/Mapillary under a Creative Commons license CC BY-SA 4.0.

Extended Data Table 1 Summary statistics of observed pedestrian counts used for model calibration
Full size table
Extended Data Table 2 Distribution of estimated pedestrian volumes across NYC boroughs and time periods
Full size table
Extended Data Table 3 Origin–destination trip types and weights used in the pedestrian flow model
Full size table

Supplementary information

Supplementary Information (download PDF )

Supplementary Tables 1 and 2 and Figs. 1–8.

Reporting Summary (download PDF )

Supplementary Data 1 (download ZIP )

Citywide pedestrian network assembled for this study and validated on 2019 aerial imagery in NYC. The network is composed of 315,577 segments, reflecting topologically connected centerlines of sidewalks, crosswalks and footpaths.

Supplementary Data 1 Metadata (download TXT )

Supplementary Data 1 Metadata

Supplementary Data 2 (download ZIP )

Calibrated models as .pckl files.

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Sevtsuk, A., Basu, R., Liu, L. et al. Spatial distribution of foot traffic in New York City and applications for urban planning. Nat Cities 3, 136–145 (2026). https://doi.org/10.1038/s44284-025-00383-y

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