Back to Search Start Over

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

Authors :
Meiqing Li
Hao Sheng
Jeremy Irvin
Heejung Chung
Andrew Ying
Tiger Sun
Andrew Y Ng
Daniel A Rodriguez
Source :
Environment and Planning B: Urban Analytics and City Science. 50:350-369
Publication Year :
2022
Publisher :
SAGE Publications, 2022.

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.

Details

ISSN :
23998091 and 23998083
Volume :
50
Database :
OpenAIRE
Journal :
Environment and Planning B: Urban Analytics and City Science
Accession number :
edsair.doi...........95f316d7b9e5db6dd52bc474be6171bb
Full Text :
https://doi.org/10.1177/23998083221112157