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Spatial-temporal features of Bicycle-sharing transportation based on Density-Based Spatial Clustering of Applications with Noise Algorithm

In response to the demand and management conflicts caused by the uneven spatial-temporal features supply and demand, such as indiscriminate parking, this paper conducts an in-depth investigation into the spatial-temporal and behavioral features of bicycle-sharing transportation, hoping to have a more accurate judgment of travel demand. Firstly, this paper conducts tide point granularity grid division and adopts python to filter the bicycle-sharing big data. Then, this paper clustered the data based on density-based spatial clustering of applications with noise (DBSCAN) method, and explored the tidal effect generated by the demand for bicycle-sharing transportation. Finally, this paper conducts statistical and descriptive analyses for the bicycle-sharing transportation data in the morning peak of weekdays. Time, urban area function, and road network are used as variables in succession to analyze the travel rules behind the spatial-temporal features of bicycle-sharing morning peak transportation.