Pedaling through the cityscape: Unveiling the association of urban environment and cycling volume through street view imagery analysis

Abstract

Cycling behavior significantly contributes to urban sustainability and enhances public health. However, revealing the relationship between the built environment and public cycling volume, particularly at the street scale, and achieving urban bicycle-friendly objectives remains a challenge due to a lack of large-scale quantitative methodologies and variability in estimation techniques. This study introduces a novel approach employing street-view imagery and machine learning technologies (specifically training deep learning models on large datasets) to overcome the limitations of traditional methods characterized by low efficiency and narrow geographic coverage. For the implementation of this method, we focus on the correlation between urban built environments and cycling volume using Amsterdam, known as a cycling haven, as a case study. The research identifies a dual interaction between street-level and surrounding greenery, manifesting in collaborative and competitive dynamics that jointly shape cycling volume. Moreover, the application of a 4D framework to assess built environments in relation to urban perceptual qualities shows significant correlations with cycling volume. To foster the development of bicycle-friendly cities and enhance public cycling practices, policymakers and urban planners may need to pay greater attention to multidimensional interventions in urban environments.