Exploring cycling flow dynamics and their interaction with built environment features: evidence from bike-sharing data

Understanding urban travel patterns through the lens of flow space is essential for analyzing urban structures and their dynamics. Cycling flows, in particular, offer valuable insights into these patterns. However, previous studies have largely overlooked the factors driving cycling flow patterns and their spatial dynamics within urban contexts. This study addresses these gaps by introducing a systematic framework to investigate cycling flow patterns and their underlying formation mechanisms. Using bike-sharing data in Shenzhen as a case study, cycling flows are categorized into three types: clustering, divergent, and convergent patterns. To quantify the nonlinear effects of various built environment features on each pattern type, interpretable machine learning models are employed. The findings reveal that cycling flow patterns reflect urban polycentric structures and functionally diverse nature of large urban environments. Additionally, the impact of built environment features depends on the context, rather than being simply positive or negative. These features also have broad and varied effects across different areas. These results provide insights for optimizing urban built environments, promoting sustainable urban mobility, and fostering balanced spatial interactions.