Examining the socio-spatial patterns of bus shelters with deep learning analysis of street-view images: A case study of 20 cities in the U.S.

Previous studies on public transit in cities found the positive role of bus shelters in promoting bus ridership. However, a large-scale and comparative investigation of bus shelter status has yet to be conducted, leaving a significant knowledge gap. To fill this gap, this research examined the socio-spatial patterns of bus shelters in 20 small- and medium-sized cities in the United States by employing a deep learning-based computer vision analysis with large-scale street-view images. The results revealed a regional difference in the bus shelter scores (range: 30.2–52.1 %). Overall, there are more bus shelters in neighborhoods with higher population densities or higher proportions of minority populations. However, there are nine cities where neighborhoods with higher proportions of minority populations are not significantly correlated with more bus shelters, suggesting issues of mobility injustice. This study is one of the first to combine an AI method with emerging urban data to examine the socio-spatial patterns of bus shelters in cities.

• Record URL:
  • https://doi.org/10.1016/j.cities.2024.104852
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0264275124000660
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/02642751
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Kim, Junghwan
  • 0000-0002-7275-769X
  • Park, Jinhyung
  • 0000-0002-6273-6182
  • Lee, Jinhyung
  • 0000-0003-1859-3441
  • Jang, Kee Moon
  • 0000-0002-4701-283X
• Publication Date: 2024-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 104852
• Serial:
  • Cities
  • Volume: 148
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0264-2751
  • Serial URL: http://www.sciencedirect.com/science/journal/02642751

Subject/Index Terms

• TRT Terms: Bus stop shelters; Cities; Image analysis; Machine learning; Ridership; Transportation equity
• Geographic Terms: United States
• Subject Areas: Planning and Forecasting; Public Transportation; Society; Terminals and Facilities;

Filing Info

• Accession Number: 01914149
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 10 2024 5:15PM