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In 2008, the CEO of the company that manages and maintains the public bicycleservice in Barcelona recognized that one may not expect to always find a placeto leave the rented bike nearby their destination, similarly to the case when,driving a car, people may not find a parking lot. In this work, we makepredictions about the statuses of the stations of the public bicycle service inBarcelona. We show that it is feasible to correctly predict nearly half of thetimes when the stations are either completely full of bikes or completelyempty, up to 2 days before they actually happen. That is, users might avoidstations at times when they could not return a bicycle that they have rentedbefore, or when they would not find a bike to rent. To achieve that, we applythe Random Forest algorithm to classify the status of the stations and improvethe lifetime of the models using publicly available data, such as informationabout the weather forecast. Finally, we expect that the results of thepredictions can be used to improve the quality of the service and make it morereliable for the users.

Predicting Occupancy Trends in Barcelona's Bicycle Service Stations Using Open Data