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Street imagery is a promising big data source providing current andhistorical images in more than 100 countries. Previous studies used this datato audit built environment features. Here we explore a novel application, usingGoogle Street View (GSV) to predict travel patterns at the city level. Wesampled 34 cities in Great Britain. In each city, we accessed GSV images from1000 random locations from years overlapping with the 2011 Census and the2011-2013 Active People Survey (APS). We manually annotated images into sevencategories of road users. We developed regression models with the counts ofimages of road users as predictors. Outcomes included Census-reported commuteshares of four modes (walking plus public transport, cycling, motorcycle, andcar), and APS-reported past-month participation in walking and cycling. Inbivariate 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-monthwalking for transport (r=0.46), GSV-motorcycles was moderately correlated withcommute share of motorcycles (r=0.44), and GSV-buses was highly correlated withcommute share of walking plus public transport (r=0.81). GSV-car was notcorrelated with car commute mode share (r=-0.12). However, in multivariableregression models, all mode shares were predicted well. Cross-validationanalyses showed good prediction performance for all the outcomes exceptpast-month walking. Street imagery is a promising new big data source topredict urban mobility patterns. Further testing across multiple settings iswarranted both for cross-sectional and longitudinal assessments.

Estimating city-level travel patterns using street imagery: a case study of using Google Street View in Britain