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

ABSTRACT:

Background

Street imagery is a promising and growing big data source providing current and historical images in more than 100 countries. Studies have reported using this data to audit road infrastructure and other built environment features. Here we explore a novel application, using Google Street View (GSV) to predict travel patterns at the city level.

Methods

We sampled 34 cities in Great Britain. In each city, we accessed 2000 GSV images from 1000 random locations. We selected archived images from time periods overlapping with the 2011 Census and the 2011-2013 Active People Survey (APS). We manually annotated the images into seven categories of road users. We developed regression models with the counts of images of road users as predictors. The outcomes included Census-re

SUBMITTER: Goel R 

PROVIDER: S-EPMC5931639 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature