Understanding bike-sharing usage and built environment influences across age groups: A spatial machine learning approach

Key Points

• Bike-sharing usage patterns significantly differ across various age groups, indicating diverse preferences.
• The analysis utilized spatial machine learning techniques to assess influences on bike-sharing behavior in urban areas.
• Understanding built environment factors is crucial for optimizing bike-sharing services and enhancing urban mobility.
• Findings may help shape urban design strategies to support increased bike-sharing participation among different ages.