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Using Machine Learning to Predict POI Occupancy to Reduce Overcrowding

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dc.contributor.authorBollenbach,Jessica
dc.contributor.authorNeubig,Stefan
dc.contributor.authorHein,Andreas
dc.contributor.authorKeller,Robert
dc.contributor.authorKrcmar,Helmut
dc.contributor.editorDemmler, Daniel
dc.contributor.editorKrupka, Daniel
dc.contributor.editorFederrath, Hannes
dc.date.accessioned2022-09-28T17:10:25Z
dc.date.available2022-09-28T17:10:25Z
dc.date.issued2022
dc.description.abstractDue 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.en
dc.identifier.doi10.18420/inf2022_34
dc.identifier.isbn978-3-88579-720-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39533
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-326
dc.subjectBeach Occupancy
dc.subjectTime series Forecast
dc.subjectXGBoost
dc.subjectRandom Forest
dc.subjectSupport Vector Regression
dc.subjectSARIMA
dc.subjectTourism Demand
dc.titleUsing Machine Learning to Predict POI Occupancy to Reduce Overcrowdingen
gi.citation.endPage408
gi.citation.startPage393
gi.conference.date26.-30. September 2022
gi.conference.locationHamburg
gi.conference.sessiontitleNachhaltige Wertschöpfungssysteme (NaWerSys) II

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P326 - INFORMATIK 2022 - Informatik in den Naturwissenschaften