Using Machine Learning to Predict POI Occupancy to Reduce Overcrowding

Title data

Bollenbach, Jessica ; Neubig, Stefan ; Hein, Andreas ; Keller, Robert ; Krcmar, Helmut:
Using Machine Learning to Predict POI Occupancy to Reduce Overcrowding.
In: Demmler, Daniel ; Krupka, Daniel ; Federrath, Hannes , Gesellschaft für Informatik e.V. (ed.): Informatik 2022 : Informatik in den Naturwissenschaften. - Bonn , 2022 . - pp. 393-408 . - (Lecture Notes in Informatics (LNI) - Proceedings ; P-326 )
ISBN 978-3-88579-720-3
DOI: https://doi.org/10.18420/inf2022_34

Project information

Abstract in another language

Due to the rapid growth of the tourism industry, associated effects like overcrowding, overtourism, and increasing greenhouse gas emissions lead to unsustainable development. A prerequisite for avoiding those adverse effects is the prediction of occupancy. The present study elaborates on the applicability and performance of various prediction models by taking a case study of beach occupancy data in Scharbeutz, Germany. The case study compares different machine learning models once as supervised machine learning models and once as time series models with a persistence model. XGBoost and Random Forest as time series demonstrate the most accurate prediction, followed by the supervised XGBoost model. However, the short prediction span of time series models is a disadvantage for longer-term visitor management to avoid the explained unsustainable effects through steering measures, so depending on the use case, the XGBoost model is to be favoured.