The effects of street environment features on road running: An analysis using crowdsourced fitness tracker data and machine learning

• Published in: Environment and planning. B, Urban analytics and city science Vol. 51; no. 2; pp. 529 - 545
• Main Authors: Zhang, Shuyang, Liu, Nianxiong, Ma, Beini, Yan, Shurui
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.02.2024
• Subjects: running-friendly street; machine learning; road running intensity; random forest regression; road running; crowdsourced data
• DOI: 10.1177/23998083231185589
• ISSN: 2399-8083

Urban streets provide environment for road running. The study proposes a non-parametric approach that uses machine learning models to predict road running intensity. The models were developed using route check-in data from Keep, a mobile exercise application, and street geographic information data in Beijing’s core district. The results show that blue space and trail continuity are the most important factors in improving road running intensity. There is an optimum design value for the sky openness and the street enclosure, which need to be balanced with shade while meeting the light of the road. And it is also important to provide appropriate visual permeability. Furthermore, unlike daily activities, it was found that higher function mixture and function density did not have significant positive effects on the road running intensity. This study provides empirical evidence on road running and highlights the key factors that planners, landscape architects, and city managers should consider when design running-friendly urban streets.