Gender disparities in bike-sharing usage: Unveiling regularity and influential factors in a large-scale campus in Tianjin, China

Bike-sharing offers convenient and eco-friendly solutions primarily for short-distance trips. However, previous research has primarily focused on urban users, with limited attention to gender-specific bike-sharing usage behavior within campus environments. Compared with urban environments, campus bike-sharing exhibits unique temporal and spatial patterns driven by class schedules, campus layout, and restricted usage zones, leading to more regular and concentrated travel behavior. Using origin-destination travel data from dockless bike-sharing system implemented on a large-scale university campus in Tianjin, China, this research examined the spatiotemporal differences in bike-sharing usage across genders. Then, the entropy rate method and machine learning models were employed to quantify bike-sharing usage regularity and identify influential factors affecting regularity, respectively. Results show that male users are 1.85 times more than females, double the ridership, and demonstrate higher regularity compared to females. Travel distance is similar between males and females, but males have faster riding speeds. Competitive relationship between genders is revealed, particularly during peak hours around student residence and academic building. Finally, policy implications are suggested to enhance the existing campus bike-sharing service and promote the equity between genders.