With the development of Internet technology and the concern of saving resources and protecting the environment, dockless bike sharing (DBS) gets rapidly popular in various cities. However, due to the vicious competition and the blind expansion in the early stage, many enterprises could not make ends meet and went bankrupt one after another. For DBS operators, one of the highest cost comes from the daily rebalancing of bikes. Therefore, this paper attempts to find whether there is a self-balance in the bike amount of DBS in the long run by analyzing the changes of the DBS bike amount. In this paper, the travel data of Mobike in Nanjing for 3 months are used. Firstly, by clustering the travel data, 4000 clustering centers are obtained and used as virtual stations. Secondly, 4000 Voronoi diagrams are created by using ArcGIS and intersected with the corresponding circles, whose radii are 500 meters, as the service scope of the virtual station. Then, the travel data of 3 months are manipulated by using SQL Server, and the variation curves of the bike amount at each station are obtained. Finally, all stations are divided into three categories (stations where bike amount increased, stations where bike amount went down and stations where bike amount were stable) by clustering the trajectory of these curves. Those where DBS bike amount can maintain a stable number are the stations that can achieve self-balance. This survey will have a beneficial impact on DBS enterprises in their daily operation and scheduling. Key words: dockless bike sharing, self-balance, travel data, virtual station, trajectory clustering

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