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Bike sharing systems' popularity has consistently been rising during the pastyears. Managing and maintaining these emerging systems are indispensable partsof these systems. Visualizing the current operations can assist in getting abetter grasp on the performance of the system. In this paper, a data miningapproach is used to identify and visualize some important factors related tobike-share operations and management. To consolidate the data, we clusterstations that have a similar pickup and drop-off profiles during weekdays andweekends. We provide the temporal profile of the center of each cluster whichcan be used as a simple and practical approach for approximating the number ofpickups and drop-offs of the stations. We also define two indices based onstations' shortages and surpluses that reflect the degree of balancing aid astation needs. These indices can help stakeholders improve the quality of thebike-share user experience in at-least two ways. It can act as a complement tobalancing optimization efforts, and it can identify stations that needexpansion. We mine the District of Columbia's regional bike-share data anddiscuss the findings of this data set. We examine the bike-share system duringdifferent quarters of the year and during both peak and non-peak hours.Findings reflect that on weekdays most of the pickups and drop-offs happenduring the morning and evening peaks whereas on weekends pickups and drop-offsare spread out throughout the day. We also show that throughout the day, morethan 40% of the stations are relatively self-balanced. Not worrying about thesestations during ordinary days can allow the balancing efforts to focus on afewer stations and therefore potentially improve the efficiency of thebalancing optimization models.

Understanding and Visualizing the District of Columbia Capital Bikeshare System Using Data Analysis for Balancing Purposes