LAUSR

Abstract In this paper, we study and implement a model of the “ToBike” bike sharing system, located in the city of Turin, Italy. The system is modeled through a closed queuing network. A thorough data analysis phase is executed on the logged dataset provided by the service provider, to assess the stationarity properties of the system and to estimate the system parameters. In particular, customer arrival rates, not directly available, are estimated from station throughput measurements through a sparse optimization technique. The parameters are then used to perform predictions of the system behavior over an unseen validation dataset. While the accuracy provided by asymptotic methods, like mean value analysis, is quite limited, numerical simulations of the closed queueing network offer viable predictions, especially when realistic behavioral patterns for the customer behavior are considered.