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Recently, an increasing number of cities have deployed bicycle-sharing systems to solve the first/last mile connection problem, generating a large quantity of data. In this paper, singular value decomposition (SVD) was used to extract the main features of the cycling flow from the origin and destination (OD) data of shared bicycles in Beijing. The results show that (1) pairs of OD flow clusters can be derived from the pairs of vectors after SVD, and each pair of clusters represents a small part of an area with dockless shared bicycles; (2) the spatial clusters derived from the top vectors of SVD are highly coincident with the hot spot areas in the heatmap of shared bicycles; (3) approximately 30% of the study area accounts for nearly 80% of bike riding; (4) nearly 70% of the clustered area derived from the top 1000 vectors of SVD is associated with subway stations; and (5) the types of point of interest (POI) differ between the origin area and destination area for the clustered area of the top 1000 vectors.

Analysis of the Cycling Flow Between Origin and Destination for Dockless Shared Bicycles Based on Singular Value Decomposition