NYC Foot Traffic: Urban Planning & Spatial Distribution

by Archynetys News Desk
  • Mallett, W. J. The Highway Funding Formula: History and Current Status Under the Infrastructure Investment and Jobs Act. Report No. R47922 (Congressional Research Service, 2024).

  • Office of Boston City Councilor Michelle Wu. Planning for a Boston Green New Deal and Just Recovery. Report No. 8 (Office of Boston City Councilor Michelle Wu, 2020).

  • Newman, P. & Kenworthy, J. The End of Automobile Dependence: How Cities Are Moving Beyond Car-Based Planning (Island Press, 2015).

  • Jaramillo, P. et al. Transport (Cambridge Univ. Press, 2022).

  • City of Boston. Go Boston 2030. (City of Boston, 2022); http://www.boston.gov/departments/transportation/go-boston-2030#report-chapters

  • City of Los Angeles. Los Angeles Green New Deal Sustainability Plan (City of Los Angeles, 2019); http://plan.mayor.lacity.gov/

  • RSG. Citywide Mobility Survey Results (NYC Department of Transportation, 2019); http://www.nyc.gov/html/dot/downloads/pdf/nycdot-citywide-mobility-survey-report-2019.pdf

  • New York City. New York City’s Roadmap to 80× 50 (New York City, 2014); https://www.nyc.gov/assets/sustainability/downloads/pdf/publications/New%20York%20City’s%20Roadmap%20to%2080%20x%2050_Final.pdf

  • Federal Highway Administration. Summary of Travel Trends: 2017 National Household Travel Survey (Federal Highway Administration, 2017); https://nhts.ornl.gov/assets/2017_nhts_summary_travel_trends.pdf

  • NYC DOT. Citywide Mobility Survey (NYC DOT, 2022); https://www.nyc.gov/html/dot/html/about/citywide-mobility-survey.shtml

  • Cooper, C. H. V. et al. Using multiple hybrid spatial design network analysis to predict longitudinal effect of a major city centre redevelopment on pedestrian flows. Transportation 48643–672 (2021).

  • Sevtsuk, A. et al. We shape our buildings, but do they then shape us? A longitudinal analysis of pedestrian flows and development activity in Melbourne. PLoS ONE 16e0257534 (2021).

  • Sevtsuk, A. et al. Pedestrian-oriented development in Beirut: a framework for estimating urban design impacts on pedestrian flows through modeling, participatory design, and scenario analysis. Cities 150104927 (2024).

  • Zhang, Q. et al. Moped meets MITO: a hybrid modeling framework for pedestrian travel demand. Transportation 501139–1165 (2023).

    Google Scholar

  • Miranda-Moreno, L. F. et al. Modeling of pedestrian activity at signalized intersections: land use, urban form, weather, and spatiotemporal patterns. Transp. Res. Rec.: J. Transp. Res. Board 226474–82 (2011).

    Article 

    Google Scholar

  • Pont, M. et al. Development of urban types based on network centrality, built density and their impact on pedestrian movement. Environ. Plan. B: Urban Anal. City Sci. 461549–1564 (2019).

    Google Scholar

  • Bolin, D. et al. Functional ANOVA modelling of pedestrian counts on streets in three European cities. J. R. Stat. Soc. A 1841176–1198 (2021).

    Article 

    Google Scholar

  • Batty, M. Agent-Based Pedestrian Modelling (ESRI Press, 2003).

  • PTV. Pedestrians’ big debut in traffic simulation: from bit player to main character. PTV Compass 14–8 (2008).

  • Axhausen, K. The Multi-Agent Transport Simulation MATSim (MATSim.org, 2016).

  • Carpenter, R. et al. in Advances in Simulation 547–557 (Springer, 2018).

  • Clifton, K. J., Singleton, P. A., Muhs, C. D. & Schneider, R. J. Representing pedestrian activity in travel demand models: framework and application. J. Transp. Geogr. 52111–122 (2016).

    Article 

    Google Scholar

  • Cooper, C. H. V. et al. sDNA: 3-D spatial network analysis for GIS, CAD, command line & Python. SoftwareX 12100525 (2020).

    Article 

    Google Scholar

  • Sevtsuk, A. & Kalvo, R. Modeling pedestrian activity in cities with urban network analysis. Environ. Plan. B: Urban Anal. City Sci. 526 (2024a).

    Google Scholar

  • Sevtsuk, A. et al. Madina Python package: scalable urban network analysis for modeling pedestrian and bicycle trips in cities. J. Transp. Geogr. 123104130 (2025).

  • Zafri, N. & Sevtsuk, A. Advancing pedestrian models: a comparative review and vision for the future. J. Am. Plan. Assoc. (in the press).

  • Turner, S. M. et al. Guide for Scalable Risk Assessment Methods for Pedestrians and Bicyclists (United States Department of Transportation, 2018); https://rosap.ntl.bts.gov/view/dot/43673

  • Jang, K. et al. Evaluation of pedestrian safety. Transp. Res. Rec.: J. Transp. Res. Board 2393104–116 (2013).

    Article 

    Google Scholar

  • Stipancic, J., Miranda-Moreno, L., Strauss, J. & Labbe, A. urélie Pedestrian safety at signalized intersections: modelling spatial effects of exposure, geometry and signalization on a large urban network. accident Anal. previous 134105265 (2020).

    Article 

    Google Scholar

  • Dobler, G., Vani, J. & Dam, T. T. L. Patterns of urban foot traffic dynamics. Comput. Environ. Urban Syst. 89101674 (2021).

    Article 

    Google Scholar

  • Angel, A. & Plaut, P. Tempo-spatial analysis of pedestrian movement in the built environment based on crowdsourced big data. Cities 149104917 (2024).

    Article 

    Google Scholar

  • Wang, X. et al. Predicting the city foot traffic with pedestrian sensor data. In Proc. 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services 1–10 (ACM, 2017).

  • Sevtsuk, A. Estimating pedestrian flows on street networks: revisiting the betweenness index. J. Am. Plan. Assoc. 87512–526 (2021).

    Article 

    Google Scholar

  • Sevtsuk, A. & Kalvo, R. Modeling pedestrian activity in cities with urban network analysis. Environ. Plan. B: Urban Anal. City Sci. 526 (2024b).

    Google Scholar

  • Ortúzar, J. de Dios et al. Modelling Transport 4th edn (Wiley, 2011).

  • Norton, P. D. Street rivals: jaywalking and the invention of the motor age street. Technol. Cult. 48331–359 (2007).

    Article 

    Google Scholar

  • Erhardt, GD et al. Traffic Forecasting Accuracy Assessment Research (Transportation Research Board, 2020); https://doi.org/10.17226/25637

  • NYC DOT. Pedestrian Mobility Plan: Pedestrian Demand (NYC DOT, 2020); https://www.nyc.gov/html/dot/html/pedestrians/pedestrian-mobility.shtml

  • New York City. Vision Zero: Building a Safer City (New York City, 2023); https://www.nyc.gov/content/visionzero/pages/.

  • NYC DOT. Vision Zero View (NYC DOT, 2025); https://vzv.nyc.

  • NYC DOT. Motor Vehicle Collisions—Crashes (NYC DOT, 2025); https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95/about_data

  • Sevtsuk, A. et al. The role of turns in pedestrian route choice: a clarification. J. Transp. Geogr. 104103392 (2022).

  • Colaninno, N. et al. A sidewalk-level urban heat risk assessment framework using pedestrian mobility and urban microclimate modeling. Environ. Plan. B: Urban Anal. City Sci. 521071–1090 (2025).

    Google Scholar

  • Institute of Transportation Engineers. Trip Generation Handbook 3rd edn (Institute of Transportation Engineers, 2016).

  • Huff, D. A probabilistic analysis of shopping center trade areas. Land Econ. 3981–90 (1963).

  • Bongiorno, C. et al. Vector-based pedestrian navigation in cities. Nat. Comput. Sci. 1678–685 (2021).

    Article 

    Google Scholar

  • Pushkarev, B. et al. Urban Space for Pedestrians (MIT Press, 1975).

  • Sevtsuk, A., Basu, R., Li, X. & Kalvo, R. A big data approach to understanding pedestrian route choice preferences—evidence from San Francisco. Travel Behav. Soc. 2541–51 (2021b).

    Article 

    Google Scholar

  • Basu, R. & Sevtsuk, A. How do street attributes affect willingness-to-walk? City-wide pedestrian route choice analysis using big data from Boston and San Francisco. Transport. Res. A 1631–19 (2022).

    Google Scholar

  • Basu, R., Colaninno, N., Alhassan, A. & Sevtsuk, A. Hot and bothered: exploring the effect of heat on pedestrian route choice behavior and accessibility. Cities 155105435 (2024).

    Article 

    Google Scholar

  • NYC DOT. Transportation Information Management System (NYC DOT, 2019); https://dottims.nyc.gov/homepage

  • US Census Bureau. Census Data 2020 (US Census Bureau, 2020); https://data.census.gov/all?q=NYC%20Census%20blocks

  • Infogroup. Infogroup US Historical Business Data 2022 (Harvard Dataverse, 2022); https://doi.org/10.7910/DVN/GW2P3G/VPA2UC

  • NYC Open Data. School Point Locations (NYC Open Data, 2019); https://data.cityofnewyork.us/Education/School-Point-Locations/jfju-ynrr/about_data

  • MTA. NYC Subway Entrances and Exits (MTA, 2024); https://data.ny.gov/Transportation/MTA-Subway-Entrances-and-Exits-2024/i9wp-a4ja/about_data

  • NYC Open Data. Parks Properties (NYC Open Data, 2023); https://data.cityofnewyork.us/Recreation/Parks-Properties/enfh-gkve/about_data

  • NYC DOT. LION (NYC DOT, 2025); https://www.nyc.gov/content/planning/pages/resources/datasets/lion

  • United States Census Bureau. 2019 TIGER/Line® Shapefiles: Block Groups (United States Census Bureau, 2020); https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Block+Groups

  • Griswold, J. B., Medury, A., Schneider, R. J. & Grembek, O. Comparison of pedestrian count expansion methods: land use groups versus empirical clusters. Transp. Res. Rec.: J. Transp. Res. Board 267287–97 (2018).

    Article 

    Google Scholar

  • Le, M., Geedipally, S. R., Fitzpatrick, K. & Avelar, R. E. Estimating pedestrian volumes for signalized and stop-controlled intersections. Transp. Res. Rec.: J. Transp. Res. Board 2674799–808 (2020).

    Article 

    Google Scholar

  • Lee, C. et al. Correlates of walking for transportation or recreation purposes. J. Phys. Act. Health 3S77–S98 (2006).

    Article 

    Google Scholar

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