Bike sharing systems are present in several modern

cities. They provide citizens with an alternative and ecological

mode of transportation, allowing them to avoid the use of

personal car and all the problems associated with it in big

cities (

i.e.

, traffic jam, roads reserved for public transport, ...).

However, they also suffer from other problems due to their

success: some stations can be full or empty (

i.e.

, impossibility

to drop off or take a bike). Thus, to predict the use of such

system can be interesting for the user in order to help him/her

to plan his/her use of the system and to reduce the probability of

suffering of the previously presented issues. This paper presents

an analysis of various regressors from the state of the art on

an existing public dataset acquired during two years in order to

predict the global use of a bike sharing system. The prediction

is done for the next twenty-four hours at a frequency of one

hour. Results show that even if most regressors are sensitive to

over-fitting, the best performing one clearly beats the baselines.

Sport Informatics and Analytics