Abstract
Tourism is a significant branch of many world economies. Many factors influence the volume of tourist traffic and the prices of trips. There are factors that clearly affect tourism, such as COVID-19. The paper describes the methods of machine learning and process mining that allow for assessing the impact of various factors (micro, mezzo and macro) on the prices of tourist offers. The methods were used on large sets of real data from two tour operators, and the results of these studies are discussed in this paper. The research presented is part of a larger project aiming at predicting trip prices. It answers the question of which factors have the greatest impact on the price and which can be omitted in further work. Nevertheless, the dynamic world situation suggests that the ranking of factors may change and the presented universal methods may provide different results in the coming years.
Funder
National Centre for Research and Development
ETI Faculty, Gdansk University of Technology
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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