Pedestrian

Engineering

Transport engineering

Urban planning

Walkability

Operating system

Civil engineering

Computer science

Built environment

Geography

Urban design

Logistic regression

Mode (computer interface)

Machine learning

Environment and Behavior

The built environment characteristics associated with walkability range from neighborhood-level urban form factors to street-level urban design factors. However, many existing walkability indices are based on neighborhood-level factors and lack consideration for street-level factors. Arguably, this omission is due to the lack of a scalable way to measure them. This paper uses computer vision to quantify street-level factors from street view images in Atlanta, Georgia, USA. Correlation analysis shows that some streetscape factors are highly correlated with neighborhood-level factors. Binary logistic regressions indicate that the streetscape factors can significantly contribute to explaining walking mode choice and that streetscape factors can have a greater association with walking mode choice than neighborhood-level factors. A potential explanation for the result is that the image-based streetscape factors may perform as proxies for some macroscale factors while representing the pedestrian experience as seen from eye-level.

How are Neighborhood and Street-Level Walkability Factors Associated with Walking Behaviors? A Big Data Approach Using Street View Images