In bicycle sharing systems (BSSs), developing effective strategies for rebalancing bicycles in real BSS require building realistic benchmark instances from the actual usage history of rented and returned bicycles. Thus, in this study, we analyzed real BSSs in Boston; Washington, D.C.; New York; and Chicago. First, we investigated whether excess and lack of bicycles were generated for the four BSSs and found that excess and lack of bicycles existed for all BSSs. Next, to determine the temporal patterns of rented and returned BSSs bicycles, we treated the usage history data of rental and return timings as a point process. To analyze the point process data, we used a raster plot, the coefficient of variation (C_V) and the local variation (L_V) of the inter-event-intervals (IEIs) for the rental and return timings. The results of L_V suggested that the statistical characteristics of the temporal patterns of events of rented and returned bicycles among the four BSSs were similar for both weekdays and weekends, and for daytime (8:00-20:59) and all day (24h). The results also suggested that the statistical characteristics of the temporal patterns of events of rented and returned bicycles in New York follow the Poisson process and those in the other cities (Boston, Washington, D.C., and Chicago) did not necessary follow the Poisson process.