Hotspot Identification and Supply–Demand Analysis of Campus Bike-Sharing from a Place-Based Perspective: A Case Study of Wuhan University

Objectives: Refined management and intelligent scheduling of campus bike-sharing systems are essential for improving commuting efficiency and maintaining spatial order. Hotspot identification and supply–demand analysis are fundamental for optimizing campus mobility operations. However, existing studies have paid limited attention to the functional semantics of campus places and commuting-related factors, such as course schedules and dormitory locations, limiting the understanding of travel patterns dominated by teaching activities. This study aims to reveal the spatial distribution of bike-sharing usage and quantify the supply-demand imbalance associated with class-related activities from a place-based perspective. Methods: Using Wuhan University as a case study, we established a place-based analytical framework integrating bike-sharing trajectories, campus vector maps, course schedules, and dormitory access records. A semantic grid map was constructed through grid partitioning and semantic mapping, forming campus place units with explicit functional semantics. Based on this map, we identified bike-sharing travel hotspots, analyzed their spatial distribution and temporal dynamics, and quantified supply–demand imbalance in commuting flows and at teaching places under class-related scenarios. Results: A total of 457 campus places were identified across 11 functional categories, dominated by teaching and residential functions. Bike-sharing usage exhibited strong spatial concentration, with the top 10% of places accounting for 49.8% of total flows. Under class-related commuting scenarios, a significant supply–demand imbalance was observed, with overall demand exceeding supply by approximately 122.2%. Conclusions: The proposed place-based analytical framework establishes a semantic linkage between physical campus environments and human mobility behaviors, enhancing the interpretation of spatial aggregation patterns and revealing systematic imbalances under class-related activities. The findings provide quantitative evidence to support spatial optimization and intelligent redistribution of campus bike-sharing systems.