Predicting bikeshare system usage up to one day ahead

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.