The paper describes an approach to environmental data mining on a problem of public bicycle system. Environmental infromation including weather information and the bicycle station location is considered, as well as the time of the day and the number of bicycles at each hour at each station. Data mining methods are applied to predict the number of available bicycles at a certain time at a given station, to describe situations of empty and full stations and, to estimate the most common paths and usage patterns. The experiemntal evaluation of the proposed approach on real-world data gives promissing results, with machine learning mehtods achieving significantely lower error on predicting the number of bicycles compared to a baseline method.
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