Non-linear impact of built environment on metro-bike integration from an eye-level perspective: A deep learning approach with street view images

The integration of metro and bike systems has emerged as a promising climate change mitigation strategy, fostering a transition towards sustainable transportation modes within urban landscapes. Extensive research has probed the effects of neighborhood-level built environment factors such as accessibility, urban density, land use mix, and proximity to cycling infrastructure—on cycling behavior. However, the impact of eye-level built environment features, those physical and visual characteristics of urban spaces as perceived by individuals at street level, remains insufficiently explored. Using Generalized Additive Mixed Models (GAMM), we examine the effects of both neighborhood-level and eye-level built environment variables on first-mile and last-mile metro-bike integration trips during weekdays and weekends, accounting for spatial and temporal autocorrelations. The results reveal that commercial establishments, job opportunities, population density, and spatial and temporal dynamics all significantly influence metro-bike integration. Surprisingly, the presence of cycle lanes shows a weak effect on integrated metro-bike usage. In addition, non-linear relationships are observed between metro-bike usage and eye-level built environment variables such as sky ratio, greenery, and building ratio, indicating the existence of optimal levels for improving metro-bike integration. The findings emphasize the importance of considering eye-level urban aesthetics when planning for transport infrastructures and thereafter provide concrete threshold guidelines for urban planners and policymakers to better integrate cycling facilities with metro system. • Both station-level and eye-level built environment features significantly impact metro-bike integration. • Proposed a framework integrating street view images and bike-sharing data. • Non-linear relationships exist between eye-level variables and metro-bike usage. • Diurnal asymmetry in metro-bicycle integration: AM first-mile, PM last-mile prevalence. • The presence of cycle lanes shows a modest effect on integrated metro-bike usage.