Abstract
With the advantages of convenient access and free parking, dockless shared bikes are favored by the public. However, the irregular flow of dockless shared bikes poses a challenge for related research. In this paper, a clustering method based on the point of interests is proposed to find the aggregation areas of dockless shared bikes. Through the analysis of the time series location data of ofo shared bikes in Beijing, a model is proposed to describe the spatio-temporal flow of urban dockless shared bikes. The model is evaluated based on the topological features of the complex network. The results can be used to analyze the mobility characteristics of citizens, prevent emergencies and improve the urban traffic management system.
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