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