Time Series Analysis of Booking Data of a Free-Floating Carsharing System in Berlin

The most rapidly growing carsharing system in North America and Europe is the free-floating one (FFCS). In a FFCS system customers can book and return vehicles of the fleet in every place of a defined operating area. While first studies tried to characterize the user of such a system and explain the booking behavior this work focuses on the short times prediction of FFCS bookings.Booking data of the FFCS operator DriveNow in Berlin are the basis for the forecast. They enable modeling time series for vehicle bookings by hour. The forecast provides predictions for every hour of a future week. To include spatial differences of FFCS bookings forecasts are calculated for every zip code area. Two methods of time series analysis are used to compare their performance for the present data: A seasonal ARIMA model and exponential smoothing with Holt-Winters-Filter.These two models are realized each with four settings. They are based upon data of a whole year, a half-year, a quarter or just a month and compared regarding their precision and practicability. Preliminary analyses such as the spectral analysis show that FFCS booking frequencies have weekly recurring trends. Additionally, it is visible that in areas with a high booking density this level lasts for the whole time. By this, spectral analysis can be applied as a spatial clustering method.The comparison of the two tools of time series analysis yields to the Holt-Winters Filtering (HWF) as the favorite method. Finding the optimal parameters for the ARIMA models is computationally intensive and results in just equally good or even worse forecasts than with exponential smoothing.The best prediction is performed with Holt-Filters-Filtering using three months of booking data. The forecast predicts bookings with an average error of only 0.84 vehicles per hour. The largest average absolute error of all compared forecast models is around 20% higher but makes the model still useful in practice though.