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.

Abstract

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.