Using Machine Learning to Predict POI Occupancy to Reduce Overcrowding
| dc.contributor.author | Bollenbach,Jessica | |
| dc.contributor.author | Neubig,Stefan | |
| dc.contributor.author | Hein,Andreas | |
| dc.contributor.author | Keller,Robert | |
| dc.contributor.author | Krcmar,Helmut | |
| dc.contributor.editor | Demmler, Daniel | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Federrath, Hannes | |
| dc.date.accessioned | 2022-09-28T17:10:25Z | |
| dc.date.available | 2022-09-28T17:10:25Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | 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. | en |
| dc.identifier.doi | 10.18420/inf2022_34 | |
| dc.identifier.isbn | 978-3-88579-720-3 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39533 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2022 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-326 | |
| dc.subject | Beach Occupancy | |
| dc.subject | Time series Forecast | |
| dc.subject | XGBoost | |
| dc.subject | Random Forest | |
| dc.subject | Support Vector Regression | |
| dc.subject | SARIMA | |
| dc.subject | Tourism Demand | |
| dc.title | Using Machine Learning to Predict POI Occupancy to Reduce Overcrowding | en |
| gi.citation.endPage | 408 | |
| gi.citation.startPage | 393 | |
| gi.conference.date | 26.-30. September 2022 | |
| gi.conference.location | Hamburg | |
| gi.conference.sessiontitle | Nachhaltige Wertschöpfungssysteme (NaWerSys) II |
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