Transit station area walkability: Identifying impediments to walking using scalable, recomputable land-use measures




first/last mile problem, viewsheds, walkability, transit, openstreetmap


Transit station area land-use characteristics can increase or decrease the perceived costs of riding rail relative to driving or taking other modes. This paper focuses on those characteristics that create discomfort to riders who are walking between stations and destinations, with the aim of providing researchers and planners with a tool that can be used to identify pain points in any existing or potential station areas. We propose and demonstrate a scalable, recomputable method of measuring pedestrian quality for trips that relies solely on datasets readily available for almost any location in the United States, and we compare results using data from a global source, OpenStreetMap. We illustrate our tool in neighborhoods surrounding the Blue Line light rail in Minneapolis, Minnesota, calculating the population-weighted distribution of land uses within pathway buffers of walks from stations to nearby destinations. We focus on land uses that pose a disutility to pedestrians such as major highways or industrial tracts, and we compare disamenity levels across station areas. Despite their simplicity, our measures capture important differences in land-use-related pedestrian experiences and reveal the inadequacy of using circular buffers to designate and characterize station catchment areas.


Adkins, A., Dill, J., Luhr, G., & Neal, M. (2012). Unpacking walkability: Testing the influence of urban design features on perceptions of walking environment attractiveness. Journal of Urban Design, 17(4), 499–510.

Agustini, K. A. V., & West, S. E. (2022). Redevelopment along arterial streets: The effects of light rail on land use change. Real Estate Economics, 51(4), 891–930.

Barrington-Leigh, C., & Millard-Ball, A. (2017). The world’s user-generated road map is more than 80% complete. PLOS ONE, 12(8), e0180698.

Basu, N., Haque, Md. M., King, M., Kamruzzaman, Md., & Oviedo-Trespalacios, O. (2022). A systematic review of the factors associated with pedestrian route choice. Transport Reviews, 42(5), 672–694.

Bhat, C. R., & Guo, J. Y. (2007). A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. Transportation Research Part B: Methodological, 41(5), 506–526.

Boarnet, M. G., Day, K., Alfonzo, M., Forsyth, A., & Oakes, M. (2006). The Irvine–Minnesota inventory to measure built environments: Reliability tests. American Journal of Preventive Medicine, 30(2), 153–159.

Boarnet, M. G., Forsyth, A., Day, K., & Oakes, J. M. (2011). The street-level built environment and physical activity and walking: Results of a predictive validity study for the Irvine Minnesota inventory. Environment and Behavior, 43(6), 735–775.

Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126–139.

Boeing, G. (2020). A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood. Environment and Planning B: Urban Analytics and City Science, 47(4), 590–608.

Boeing, G. (2021). Street network models and indicators for every urban area in the world. Geographical Analysis, 54(3), 519–535.

Boeing, G., Higgs, C., Liu, S., Giles-Corti, B., Sallis, J. F., Cerin, E., … Arundel, J. (2022). Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities. The Lancet Global Health, 10(6), e907–e918.

Bright, J., De Sabbata, S., Lee, S., Ganesh, B., & Humphreys, D. K. (2018). OpenStreetMap data for alcohol research: Reliability assessment and quality indicators. Health & Place, 50, 130–136.

Brovelli, M. A., & Zamboni, G. (2018). A new method for the assessment of spatial accuracy and completeness of OpenStreetMap building footprints. ISPRS International Journal of Geo-Information, 7(8), 289–313.

Brown, B. B., Werner, C. M., Amburgey, J. W., & Szalay, C. (2007). Walkable route perceptions and physical features: Converging evidence for en route walking experiences. Environment and Behavior, 39(1), 34–61.

Calthorpe, P. (1989). The pedestrian pocket. Pedestrian Pocket Book, 350–356.

Cao, X. (2015). Residential preference and choice of movers in light rail neighborhoods in Minneapolis, Minnesota. Transportation Research Record, 2494(1), 1–10.

Cao, X., & Schoner, J. (2014). The influence of light rail transit on transit use: An exploration of station area residents along the Hiawatha line in Minneapolis. Transportation Research Part A: Policy and Practice, 59, 134–143.

Cervero, R. (2002). Built environments and mode choice: Toward a normative framework. Transportation Research Part D: Transport and Environment, 7(4), 265–284.

Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199–219.

Cetintahra, G. E., & Cubukcu, E. (2015). The influence of environmental aesthetics on economic value of housing: An empirical research on virtual environments. Journal of Housing and the Built Environment, 30(2), 331–340.

Day, K., Boarnet, M., Alfonzo, M., & Forsyth, A. (2006). The Irvine–Minnesota inventory to measure built environments: Development. American Journal of Preventive Medicine, 30(2), 144–152.

Ding, C., Cao, X., & Liu, C. (2019). How does the station-area built environment influence metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds. Journal of Transport Geography, 77, 70–78.

Ewing, R., & Cervero, R. (2001). Travel and the built environment: A synthesis. Transportation Research Record, 1780(1), 87–114.

Ewing, R., & Cervero, R. (2010). Travel and the built environment. Journal of the American Planning Association, 76(3), 265–294.

Fan, Y., Guthrie, A., & Levinson, D. (2012). Impact of light-rail implementation on labor market accessibility: A transportation equity perspective. Journal of Transport and Land Use, 5(3), 28–39.

Federal Transit Administration. (2011). Final policy statement on the eligibility of pedestrian and bicycle improvements under federal transit law. Federal Register, 76(1), 52046–52053.

Federal Transit Administration. (2017). Manual on pedestrian and bicycle connections to transit (Report 0111). Retrieved from

Gehrke, S. R., & Clifton, K. J. (2019). An activity-related land-use mix construct and its connection to pedestrian travel. Environment and Planning B: Urban Analytics and City Science, 46(1), 9–26.

Givoni, M., & Rietveld, P. (2007). The access journey to the railway station and its role in passengers’ satisfaction with rail travel. Transport Policy, 14(5), 357–365.

Guo, Z., & Ferreira, J. J. (2008). Pedestrian environments, transit path choice, and transfer penalties: Understanding land-use impacts on transit travel. Environment and Planning B, 35(3), 461–479.

Gutiérrez, J., Cardozo, O. D., & García-Palomares, J. C. (2011). Transit ridership forecasting at station level: An approach based on distance-decay weighted regression. Journal of Transport Geography, 19(6), 1081–1092.

Hartig, T., Evans, G. W., Jamner, L. D., Davis, D. S., & Gärling, T. (2003). Tracking restoration in natural and urban field settings. Journal of Environmental Psychology, 23(2), 109–123.

Hurst, N. B., & West, S. E. (2014). Public transit and urban redevelopment: The effect of light rail transit on land use in Minneapolis, Minnesota. Regional Science and Urban Economics, 46, 57–72.

Jacobs, J. (1961). The curse of border vacuums. In The death and life of great American cities. New York: Random House.

Jun, M.-J., Choi, K., Jeong, J.-E., Kwon, K.-H., & Kim, H.-J. (2015). Land-use characteristics of subway catchment areas and their influence on subway ridership in Seoul. Journal of Transport Geography, 48, 30–40.

LaJeunesse, S., Ryus, P., Kumfer, W., Kothuri, S., & Nordback, K. (2021). Measuring pedestrian level of stress in urban environments: Naturalistic walking pilot study. Transportation Research Record, 2675(10), 109–119.

Liu, J., Xiao, L., & Zhou, J. (2021). Built environment correlates of walking for transportation. Journal of Transport and Land Use, 14(1), 1129–1148.

Liu, Y., Yang, D., Timmermans, H. J. P., & de Vries, B. (2020). The impact of the street-scale built environment on pedestrian metro station access/egress route choice. Transportation Research Part D: Transport and Environment, 87, 102491.

Loukaitou-Sideris, A. (2006). Is it safe to walk? Neighborhood safety and security considerations and their effects on walking. Journal of Planning Literature, 20(3), 219–232.

Marshall, W. E., Garrick, N. W., & Hansen, G. (2008). Reassessing on-street parking. Transportation Research Record, 2046(1), 45–52.

Minnesota Geospatial Commons. (2022). Transit stops—Minnesota Geospatial Commons. Retrieved from

Mulley, C., Tsai, C.-H. (Patrick), & Ma, L. (2018). Does residential property price benefit from light rail in Sydney? Research in Transportation Economics, 67, 3–10.

Naik, N., Raskar, R., & Hidalgo, C. A. (2016). Cities are physical too: Using computer vision to measure the quality and impact of urban appearance. American Economic Review, 106(5), 128–132.

Park, K., Farb, A., & Chen, S. (2021). First- and last-mile experience matters: The influence of the built environment on satisfaction and loyalty among public transit riders. Transport Policy, 112, 32–42.

Park, S., Deakin, E., & Lee, J. S. (2014). Perception-based walkability index to test impact of microlevel walkability on sustainable mode choice decisions. Transportation Research Record, 2464(1), 126–134.

Pilgram, C. A., & West, S. E. (2018). Fading premiums: The effect of light rail on residential property values in Minneapolis, Minnesota. Regional Science and Urban Economics, 69, 1–10.

Redfearn, C. L. (2009). How informative are average effects? Hedonic regression and amenity capitalization in complex urban housing markets. Regional Science and Urban Economics, 39(3), 297–306.

Renne, J. L., & Appleyard, B. (2019). Twenty-five years in the making: TOD as a new name for an enduring concept. In Journal of Planning Education and Research, 39(4), 402–408.

Thomas, J., & Reyes, R. (2021). National walkability index, methodology and user guide. Washington, DC: United States Environmental Protection Agency (EPA). Retrieved from https://www.

Tribby, C. P., Miller, H. J., Brown, B. B., Werner, C. M., & Smith, K. R. (2017). Analyzing walking route choice through built environments using random forests and discrete choice techniques. Environment and Planning B, 44(6), 1145–1167.

Venter, C. J. (2020). Measuring the quality of the first/last mile connection to public transport. Research in Transportation Economics, 83, 100949.

Werner, C. M., Brown, B. B., & Gallimore, J. (2010). Light rail use is more likely on “walkable” blocks: Further support for using micro-level environmental audit measures. Journal of Environmental Psychology, 30(2), 206–214.

Yang, L., Chau, K. W., Szeto, W. Y., Cui, X., & Wang, X. (2020). Accessibility to transit, by transit, and property prices: Spatially varying relationships. Transportation Research Part D: Transport and Environment, 85, 102387.

Yin, L., Cheng, Q., Wang, Z., & Shao, Z. (2015). ‘Big data’ for pedestrian volume: Exploring the use of Google Street View images for pedestrian counts. Applied Geography, 63, 337–345.

Yin, L., & Wang, Z. (2016). Measuring visual enclosure for street walkability: Using machine learning algorithms and Google Street View imagery. Applied Geography, 76, 147–153.

Zellner, M., Massey, D., Shiftan, Y., Levine, J., & Arquero, M. J. (2016). Overcoming the last-mile problem with transportation and land-use improvements: An agent-based approach. International Journal of Transportation, 4(1), 1–26.

Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018). Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning, 180, 148–160.

Zhang, Q., Moeckel, R., & Clifton, K. (2022). Assessing pedestrian impacts of future land use and transportation scenarios. Journal of Transport and Land Use, 15(1), 547–566.

Zhou, Q., Wang, S., & Liu, Y. (2022). Exploring the accuracy and completeness patterns of global land-cover/land-use data in OpenStreetMap. Applied Geography, 145, 102742.




How to Cite

Pilgram, C., & West, S. (2023). Transit station area walkability: Identifying impediments to walking using scalable, recomputable land-use measures. Journal of Transport and Land Use, 16(1), 267–292.