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Marked crosswalks in US transit-oriented station areas, 2007–2020: A computer vision approach using street view imagery

Author

Listed:
  • Meiqing Li
  • Hao Sheng
  • Jeremy Irvin
  • Heejung Chung
  • Andrew Ying
  • Tiger Sun
  • Andrew Y Ng
  • Daniel A Rodriguez

Abstract

Improving the built environment to support walking is a popular strategy to increase urban sustainability and walkability. In the past decade alone, many US cities have implemented crosswalk visibility enhancement programs as part of road safety improvements and active transportation plans. However, there are no systematic ways of measuring and monitoring the presence of key built environment attributes that influence the safety and walkability of an area, such as marked crosswalks. Furthermore, little is known about how these attributes change over time at a national scale. In this paper, we introduce an innovative approach using a deep learning-based computer vision model on Street View images to identify changes in intersection-level marked crosswalks around more than 4,000 US transit stations over a 14-year period. We found an increase in the overall number of marked crosswalks at intersections. Furthermore, high-visibility crosswalks became more common, as they replaced existing parallel-line crosswalks. We further examine crosswalks around transit stations in New York City and San Francisco to illustrate geographic variations and compare associations with other characteristics of the built environment as reported in the Smart Location Database. Areas with increases in high-visibility crosswalks focused on high density residential areas and areas with a higher percent of zero-vehicle households. However, geographic variations exist. For example, in San Francisco, transit station areas outside downtown or major corridors (South and Southwest of the city) had the lower prevalence of marked crosswalks. This analysis confirms important gaps in crosswalk visibility that call for safety enhancements and opens the door for additional research involving these data. We conclude by discussing the limitations and future research opportunities using computer vision to automatically detect large-scale transportation infrastructure changes at a relatively low cost.

Suggested Citation

  • Meiqing Li & Hao Sheng & Jeremy Irvin & Heejung Chung & Andrew Ying & Tiger Sun & Andrew Y Ng & Daniel A Rodriguez, 2023. "Marked crosswalks in US transit-oriented station areas, 2007–2020: A computer vision approach using street view imagery," Environment and Planning B, , vol. 50(2), pages 350-369, February.
  • Handle: RePEc:sae:envirb:v:50:y:2023:i:2:p:350-369
    DOI: 10.1177/23998083221112157
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    References listed on IDEAS

    as
    1. Moran, Marcel E., 2022. "Where the Crosswalk Ends: Mapping Crosswalk Coverage via Satellite Imagery in San Francisco," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt67447864, Institute of Transportation Studies, UC Berkeley.
    2. Marcel E. Moran, 2022. "Where the Crosswalk Ends: Mapping Crosswalk Coverage via Satellite Imagery in San Francisco," Environment and Planning B, , vol. 49(8), pages 2250-2266, October.
    3. Mitman, Meghan Felig & Ragland, David R & Zegeer, Charles V., 2008. "The Marked Crosswalk Dilemma: Uncovering Some Missing Links in a 35-Year Debate," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt13r7q036, Institute of Transportation Studies, UC Berkeley.
    4. Mooney, S.J. & DiMaggio, C.J. & Lovasi, G.S. & Neckerman, K.M. & Bader, M.D.M. & Teitler, J.O. & Sheehan, D.M. & Jack, D.W. & Rundle, A.G., 2016. "Use of google street view to assess environmental contributions to pedestrian injury," American Journal of Public Health, American Public Health Association, vol. 106(3), pages 462-469.
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    Cited by:

    1. Puxuan Zhang & Yichen Liu & Yihua Huang, 2025. "Dynamic Assessment of Street Environmental Quality Using Time-Series Street View Imagery Within Daily Intervals," Land, MDPI, vol. 14(8), pages 1-19, July.

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