Bikesharing systems are becoming popular all over the world. One of the remaining problems is that the rides are not uniformly distributed between stations and that certain stations fill up or empty over time. These empty and full stations lead to demand for bikes and return boxes (docks) that cannot be fulfilled; the situation leads to unsatisfied and possibly even lost customers. To avoid this situation, the provider redistributes bikes in the system. Although redistribution of bikes in such systems is well studied, the underlying demand has not yet been modeled to serve as an input to improve the redistribution. For this gap to be closed, demand for bikes and return boxes was modeled with data from the bikesharing system Citybike Wien in Vienna, Austria. In particular, the influence of weather and full or empty neighboring stations on demand was studied by using different count models. Furthermore, historic demand was used to show that forecasts from the model had improved. Last, the influence of new stations on the model parameters of a station and resultant structural breaks in the model were discussed.