Résumé | 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. |