Characterizing the Dynamics of Dockless Sharing Bicycle Program in Shanghai by Using Large Scale Data

Dockless bicycle sharing systems in Shanghai cause unbalanced trips and abandoned bicycles in the street. Few efforts have explored dockless bicycles using stochastic trip and operational data. In this paper, we combine stated survey and data mining for GPS operation data. Six consecutive days of data from 9.93 million records of shared bicycle bands operating in Shanghai were collected. First, trip distance and travel time distribution was compared with stated surveys. Then temporal and spatial variation patterns of shared cycling trips in Shanghai were presented, finding peak periods on workdays and trips mostly to the city center close to metro stations. A clustering algorithm looked for commonalities among the areas of cycling ridership and other geographical features, and classified five groups. Unbalanced trips may result in changes in bicycle supply during peak hours. This study offers suggestions to develop this industry sustainaibly.