| Summary: | Bike-sharing systems (BSS) are becoming very popular and, consequently, their management is becoming more complex. The main objective of this dissertation is the development of a decision support service for Bewegen bike-sharing systems applying machine learning (ML) methods. An additional objective is the development of an appropriate mechanism for systematic data collection, required in the development and test of the ML methods. The decision support service has two goals. The first goal is the prediction of the number of bikes in each station 30 minutes ahead of time, to be provided to the bike-sharing system clients. The second goal is the prediction of the number of bikes in each station 24 hours ahead of time, to be provided to the bike-sharing operators when deciding how to redistribute bikes among the different stations. In order to reach these two goals, two ML approaches were implemented: a neural network (NN) model and a k-nearest neighbour (k-NN) algorithm. The tests have shown that the NN algorithms provide better prediction results on both goals. The prediction algorithms were trained and tested with collected historical data from one of the Bewegen's BSS from 1 of January, 2019 until 30 of April, 2019.
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