  Street imagery is a promising big data source providing current and
historical images in more than 100 countries. Previous studies used this data
to audit built environment features. Here we explore a novel application, using
Google Street View (GSV) to predict travel patterns at the city level. We
sampled 34 cities in Great Britain. In each city, we accessed GSV images from
1000 random locations from years overlapping with the 2011 Census and the
2011-2013 Active People Survey (APS). We manually annotated images into seven
categories of road users. We developed regression models with the counts of
images of road users as predictors. Outcomes included Census-reported commute
shares of four modes (walking plus public transport, cycling, motorcycle, and
car), and APS-reported past-month participation in walking and cycling. In
bivariate analyses, we found high correlations between GSV counts of cyclists
(GSV-cyclists) and cycle commute mode share (r=0.92) and past-month cycling
(r=0.90). Likewise, GSV-pedestrians was moderately correlated with past-month
walking for transport (r=0.46), GSV-motorcycles was moderately correlated with
commute share of motorcycles (r=0.44), and GSV-buses was highly correlated with
commute share of walking plus public transport (r=0.81). GSV-car was not
correlated with car commute mode share (r=-0.12). However, in multivariable
regression models, all mode shares were predicted well. Cross-validation
analyses showed good prediction performance for all the outcomes except
past-month walking. Street imagery is a promising new big data source to
predict urban mobility patterns. Further testing across multiple settings is
warranted both for cross-sectional and longitudinal assessments.
