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

arXiv: https://arxiv.org/abs/1802.02915 · HTML: https://mlsanity.com/paper/1802.02915/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2018.

## Built on by (most cited first, 10 of 10)

- [Assessing bikeability with street view imagery and computer vision](https://mlsanity.com/paper/2105.08499/) (2021)
- [Self-supervised learning unveils change in urban housing from street-level images](https://mlsanity.com/paper/2309.11354/) (2023)
- [Urban Visual Intelligence: Studying Cities with AI and Street-level Imagery](https://mlsanity.com/paper/2301.00580/) (2023)
- [Street design and driving behavior: evidence from a large-scale study in Milan, Amsterdam, and Dubai](https://mlsanity.com/paper/2507.04434/) (2025)
- [Deep Learning-based Scalable Image-to-3D Facade Parser for Generating Thermal 3D Building Models](https://mlsanity.com/paper/2508.04406/) (2025)
- [Vehicle detection from GSV imagery: Predicting travel behaviour for cycling and motorcycling using Computer Vision](https://mlsanity.com/paper/2508.12794/) (2025)
- [Joint Multi-Condition Representation Modelling via Matrix Factorisation for Visual Place Recognition](https://mlsanity.com/paper/2510.17739/) (2025)
- [AI vs Human Expert Reasoning: Assessing Agreements in Building Typology Predictions based on Street View Imagery](https://mlsanity.com/paper/2607.14756/) (2026)
- [Examining the Associations between Visual and Non-Visual Elements and Cyclists' Route Choices for Various Trip Purposes](https://mlsanity.com/paper/2607.15808/) (2026)
- [A Vision-Language Framework for Measuring Social Life on Sidewalks](https://mlsanity.com/paper/2609.28476/) (2026)

## Abstract (arXiv)

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.