Estimating pedestrian volume using Street View images : A large-scale validation test

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

10 Scopus Citations
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Author(s)

  • Qiang Sheng
  • Yu Ye
  • Ruoyu Wang
  • Ye Liu

Detail(s)

Original languageEnglish
Article number101481
Journal / PublicationComputers, Environment and Urban Systems
Volume81
Online published11 Mar 2020
Publication statusPublished - May 2020

Abstract

Pedestrian volume is an important indicator of urban walkability and vitality. Hence, information on pedestrian volumes of different streets is indispensable for creating healthy, pedestrian-oriented cities. Pedestrian volume data have traditionally been collected through field observations, which has many methodological limitations, e.g. time-consuming, labor-intensive, and inefficient.

Assessing pedestrian volume automatically from Street View images (SVIs) with machine learning techniques can overcome such limitations because this approach offers a wide geographic reach and consistent image acquisition. Nevertheless, this new method has not been rigorously validated, and its accuracy remains unclear.

In this study, we conducted a large-scale validation test by comparing pedestrian volume extracted from SVIs with the results from field observations for more than 700 street segments in Tianjin, China. A total of 4507 sampling points along these street segments were used to collect SVIs.

The results demonstrated that using SVIs with machine learning techniques is a promising method for estimating pedestrian volumes with a large geographic reach. Automated pedestrian volume detection could achieve reasonable (Cronbach's alpha ≥0.70) or good (Cronbach's alpha ≥0.80) levels of accuracy. It is worth noting that various factors of SVIs and street segments may affect the accuracy. SVIs with higher image quality, larger image size, and collection times closer to the targeted periods produced more accurate results. The automated method also worked better in areas with high pedestrian volume and high street connectivity.

Research Area(s)

  • Big data, Machine learning, Pedestrian volume, Street view images