Parking Situation Analysis and Hotspots Identification of Shared Bicycles

As a low-carbon transportation mode, dockless bicycle sharing plays a crucial role in urban transportation. However, the tidal phenomenon of shared bicycles and the imbalance between supply and demand have brought many problems to users, shared bicycle enterprises, and urban managers, such as disorderly parking, difficulty in borrowing and returning bicycles. This study is based on the data from the bicycle sharing system in the Huli and Siming districts of Xiamen. After data preprocessing, the Geohash algorithm is used to match shared bicycles with electronic fences to analyze whether the parking location is within the boundary range. Then, the HDBSCAN algorithm is used to cluster parking areas. Finally, based on the retention quantity and retention density, the parking hotspots are identified and the characteristics of these areas are analyzed. The relevant results provide reference for the management department to achieve efficient and standardized operation of shared bicycles.