Recently, we are witnessing a true digital revolution in every sector of the industry, which is involved in mobility and transportation. As an example, we can expose American start-up Uber, which has drastically digitalized mobility in the recent years. Their business strategy is data driven; with the data mining methodologies they are trying to answer a question "From and where people will travel?", and for that they use their services. The better they can predict the movement of people through the area, the more successful and optimised their actions on the market are. In this bachelor thesis, first we will collect the data of movement of people through the city of Ljubljana, then we will form horizontally scalable system, which will enable us persistent storage and processing of the collected data. The most suitable system for this kind of project is bike sharing system located in Ljubljana, which operates under the brand BicikeLj. The question, on which we will try to answer, is how empty or full BicikeLj stations will be in a certain time in the future, and how precisely we can predict that based on the previous data.
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