Abstract
For redistribution and operating bikes in a free-floating systems, two measures are of highest priority. First, the information about the expected number of rentals on a day is an important measure for service providers for management and service of their fleet. The estimation of the expected number of bookings is carried out with a simple model and a more complex model based on meterological information, as the number of loans depends strongly on the current and forecasted weather. Secondly, the knowledge of a service level violation in future on a fine spatial resolution is important for redistribution of bikes. With this information, the service provider can set reward zones where service level violations will occur in the near future. To forecast a service level violation on a fine geographical resolution the current distribution of bikes as well as the time and space information of past rentals has to be taken into account. A Markov Chain Model is formulated to integrate this information.
 We develop a management tool that describes in an explorative way important information about past, present and predicted future counts on rentals in time and space. It integrates all estimation procedures. The management tool is running in the browser and continuously updates the information and predictions since the bike distribution over the observed area is in continous flow as well as new data are generated continuously.
Highlights
For a service provider, redistribution costs represent a significant expense for operating a free-floating vehicle fleet. Heitz, Etschmann, Stockle, Bachmann, and Templ (2019) and Lippoldt, Niels, and Bogenberger (2019) showed the effectiveness of incentivizing users via reward zones in the urban area
The time to a service level violation is based on the last state of the imported data and the estimated dropping and picking rates from the last important state of the data import, as well as the current bike distribution
The existing literature on spatial-temporal usage patterns of bike sharing systems mainly focuses on the analysis of historical bookings using descriptive statistics or models
Summary
Redistribution costs represent a significant expense for operating a free-floating vehicle fleet. Heitz, Etschmann, Stockle, Bachmann, and Templ (2019) and Lippoldt, Niels, and Bogenberger (2019) showed the effectiveness of incentivizing users via reward zones in the urban area. The aim is that service operators minimize the redistribution of bicycles (operator-based redistribution), by motivating users through a system of rewards to distribute the bicycles in the urban area in such a way that the service level is maximized (user-based redistribution) For setting such incentives, the current bike distribution as well as the predicted changes by picking and dropping of bikes has to be known (see Heitz et al 2019). Fleet Management in Free-Floating Bike Sharing Systems distribution of future picks and drops, respectively Both Heitz et al (2019) and Lippoldt et al (2019) didn’t show nor integrated forecasts in their model based on all available information - the current bike distribution and historical booking data.
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